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Machine learning
Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.
News & Views | 21 February 2023
From the archive: machine intelligence, and the father of X-rays
News & Views | 20 February 2023
Sensing the shape of functional proteins with topology
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Evaluation of grouped capsule network for intracranial hemorrhage segmentation in CT scans
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Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes
- Joomee Song
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Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and discriminative spot representations from spatial transcriptomics data.
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Research 28 February 2023 | Open Access
Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods
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Protocols | 27 February 2023
cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA
This protocol describes cfSNV, a user-friendly software package that comprehensively considers the unique properties of cell-free DNA for the sensitive detection of somatic mutations from blood samples.
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Research 27 February 2023 | Open Access
Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury
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Correspondence | 28 February 2023
OpenAI — explain why some countries are excluded from ChatGPT
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Research Highlights | 27 February 2023
Human–AI collaboration boosts mental health support
Hailey, an AI feedback tool, helps online mental health support workers to respond with more empathy.
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Editorial | 27 February 2023
Rates against the machine
Computational chemistry has become an increasingly common part of catalysis research. More recently, data-based methods such as machine learning have been suggested as a means to speed up discovery. This Focus issue features a collection of content dedicated to machine learning as pertaining to its potential impact on the field of catalysis.
Comments & Opinion | 27 February 2023
Autonomous ships are on the horizon: here’s what we need to know
Ships and ports are ripe for operation without humans — but only if the maritime industry can work through the practical, legal and economic implications first.
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Why artificial intelligence needs to understand consequences
A machine with a grasp of cause and effect could learn more like a human, through imagination and regret.
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The aim of this paper is to introduce a new learning procedure for neural networks and to demonstrate that it works well enough on a few small problems to be worth further investigation.
Discovering faster matrix multiplication algorithms with reinforcement learning
Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2.
Composer: Creative and Controllable Image Synthesis with Composable Conditions
damo-vilab/composer • 20 Feb 2023
Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability.
Adding Conditional Control to Text-to-Image Diffusion Models
Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices.
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains.
VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
nvlabs/voxformer • 23 Feb 2023
To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images.
Multimodal Chain-of-Thought Reasoning in Language Models
Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer.
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
Spiking neural networks (SNNs) have emerged as an energy-efficient approach to deep learning that leverage sparse and event-driven activations to reduce the computational overhead associated with model inference.
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model.
OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion
3D Semantic Scene Completion (SSC) can provide dense geometric and semantic scene representations, which can be applied in the field of autonomous driving and robotic systems.
Machine Learning
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Cross-model consensus of explanations and beyond for image classification models: an empirical study
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An explainable machine learning model for identifying geographical origins of sea cucumber Apostichopus japonicus based on multi-element profile
A comparison of machine learning- and regression-based models for predicting ductility ratio of rc beam-column joints, alexa, is this a historical record.
Digital transformation in government has brought an increase in the scale, variety, and complexity of records and greater levels of disorganised data. Current practices for selecting records for transfer to The National Archives (TNA) were developed to deal with paper records and are struggling to deal with this shift. This article examines the background to the problem and outlines a project that TNA undertook to research the feasibility of using commercially available artificial intelligence tools to aid selection. The project AI for Selection evaluated a range of commercial solutions varying from off-the-shelf products to cloud-hosted machine learning platforms, as well as a benchmarking tool developed in-house. Suitability of tools depended on several factors, including requirements and skills of transferring bodies as well as the tools’ usability and configurability. This article also explores questions around trust and explainability of decisions made when using AI for sensitive tasks such as selection.
Automated Text Classification of Maintenance Data of Higher Education Buildings Using Text Mining and Machine Learning Techniques
Data-driven analysis and machine learning for energy prediction in distributed photovoltaic generation plants: a case study in queensland, australia, modeling nutrient removal by membrane bioreactor at a sewage treatment plant using machine learning models, big five personality prediction based in indonesian tweets using machine learning methods.
<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.</span>
Compressive strength of concrete with recycled aggregate; a machine learning-based evaluation
Temperature prediction of flat steel box girders of long-span bridges utilizing in situ environmental parameters and machine learning, computer-assisted cohort identification in practice.
The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. We propose an alternative approach, IQRef , in which the expert iteratively designs a classifier and the machine helps him or her to determine how well it is performing and, importantly, when to stop, by reporting statistics on a fixed, hold-out sample of annotated records. We justify our approach based on prior work giving a theoretical model of how to re-use hold-out data. We compare the two approaches in the context of identifying a cohort of EHRs and examine their strengths and weaknesses through a case study arising from an optometric research problem. We conclude that both approaches are complementary, and we recommend that they both be employed in conjunction to address the problem of cohort identification in health research.
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Machine Learning: Algorithms, Real-World Applications and Research Directions
Affiliations.
- 1 Swinburne University of Technology, Melbourne, VIC 3122 Australia.
- 2 Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, 4349 Chattogram, Bangladesh.
- PMID: 33778771
- PMCID: PMC7983091
- DOI: 10.1007/s42979-021-00592-x
In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning , which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
Keywords: Artificial intelligence; Data science; Data-driven decision-making; Deep learning; Intelligent applications; Machine learning; Predictive analytics.
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.
Conflict of interest statement
Conflict of interestThe author declares no conflict of interest.
The worldwide popularity score of…
The worldwide popularity score of various types of ML algorithms (supervised, unsupervised, semi-supervised,…
Various types of machine learning…
Various types of machine learning techniques
A general structure of a…
A general structure of a machine learning based predictive model considering both the…
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An example of a decision tree structure
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Classification vs. regression. In classification…
Classification vs. regression. In classification the dotted line represents a linear boundary that…
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A graphical interpretation of the widely-used hierarchical clustering (Bottom-up and top-down) technique
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Machine learning and deep learning performance in general with the amount of data
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Towards Data Science
Jun 30, 2021
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6 Research Papers about Machine Learning Deployment Phase
Adopting the academic mindset and habits.
A beginner's mistake is to ignore research. Reading research is daunting, especially when you’re not from an academic background, like me. Nonetheless, it ought to be done.
Ignoring research can easily lead to you falling behind with your skills set because research paints the scope of the current problems being grappled with. Therefore, to remain relevant as a machine learning practitioner involves adopting the academic mindset and habits [to some degree].
For my studies, I’ve curated 6 research papers I will be reading to learn more about machine learning deployments going forward. Here are the research papers in non-chronological order:
Challenges in Deploying Machine Learning: A Survey of Case Studies , Paleyes et al , Jan 2021
Machine learning practitioners and researchers face a number of challenges during the deployment of machine learning models in production systems.
“This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries, and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. Our survey shows that practitioners face challenges at each stage of the deployment. The goal of this paper is to lay out a research agenda to explore approaches addressing these challenges.”
Hidden Technical Debt In Machine Learning Systems , Sculley et al , Dec 2015
This is a popular paper that attempts to document the realities of machine learning in the real world from a costs perspective. The paper states “Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. This paper argues it is dangerous to think of these quick wins as coming for free.”
Essentially, the goal of this paper is to explore different ML specific risks involved with implementing machine learning in the real world.
A Systems Perspective To Reproducibility in Production Machine Learning Domain , Ghanta et al , Jun 2018
The part of machine learning that’s not always bragged about is the logistics, yet its importance is vast. In order to reproduce machine learning pipelines that have been deployed in production, machine learning practitioners must capture both the historic state of the model, as well as its current state. This is an extremely complex task, but this paper allegedly has some solutions.
“We present a system that addresses these issues from a systems perspective, enabling ML experts to track and reproduce ML models and pipelines in production. This enables quick diagnosis of issues that occur in production.”
Software Engineering for Machine Learning: A Case Study , Amershi et al , May 2019
Unlike many companies, Microsoft has been implementing machine learning for many years. From their wealth of experience, Microsoft seeks to share what they believe should serve as a set of best practices to other organizations developing AI applications and Data Science tools.
“We have identified three aspects of the AI domain that make it fundamentally different from prior software application domains:
- Discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering
- Model customization and model reuse require very different skills than are typically found in software teams
- AI components are more difficult to handle as distinct modules than traditional software components — models may be “entangled” in complex ways and experience non-monotonic error behavior.”
The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction , Breck et al , Dec 2017
Of all the papers on my list, I am least familiar with this paper (meaning I’ve only come across it recently). In the abstract, the authors state that they will “present 28 specific tests and monitoring needs, drawn from experience with a wide range of production ML systems to help quantify these issues and present an easy to follow roadmap to improve production readiness and pay down ML technical debt.”.
Building a Reproducible Machine Learning Pipeline , Sugimura. P & Hartl. F , Oct 2018
All machine learning practitioners (i.e. industry or academia) are required to build reproducible models. Failing to do so can result in significant financial loss, lost time, and loss of personal reputation if there is absolutely no way to recover past experiments. This paper covers various challenges to reproducibility, practitioners may face throughout the lifecycle of a machine learning workflow. The paper then goes on to describe a suitable framework, created by the authors, to overcome the aforementioned challenges.
“The framework is comprised of four main components (data, feature, scoring, and evaluation layers), which are themselves comprised of well-defined transformations. This enables us to not only exactly replicate a model, but also to reuse the transformations across different models. As a result, the platform has dramatically increased the speed of both offline and online experimentation while also ensuring model reproducibility.”
This list is by no means extensive. Andrew Ng suggests practitioners should read [and understand] 50–100 papers on a subject to have a very deep understanding of the requirements of the domain.
Understanding research papers does not only come from reading lots of research. You may be required to deviate between trusted resources online such as blog posts and video content. Consequently, I’ve added some valuable resources to make understanding Machine Learning deployments easier.
- How to Deploy Machine Learning Models
- Deploying Machine Learning Models in Shadow Mode
- Top 10 Software Development Fundamentals for Data Scientists
- Monitoring Machine Learning Models in Production
- Monitoring is a Means, Not an End
Many practitioners fall into the trap of thinking that they aren’t required to read research papers — this often occurs in practitioners that aren’t as academic (like me). Deciding to ignore research could easily lead to you falling behind in the field hence it’s important to adopt an academic mind and habits, whilst still applying yourself practically.
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The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots
2020’s Top AI & Machine Learning Research Papers
November 24, 2020 by Mariya Yao
Despite the challenges of 2020, the AI research community produced a number of meaningful technical breakthroughs. GPT-3 by OpenAI may be the most famous, but there are definitely many other research papers worth your attention.
For example, teams from Google introduced a revolutionary chatbot, Meena, and EfficientDet object detectors in image recognition. Researchers from Yale introduced a novel AdaBelief optimizer that combines many benefits of existing optimization methods. OpenAI researchers demonstrated how deep reinforcement learning techniques can achieve superhuman performance in Dota 2.
To help you catch up on essential reading, we’ve summarized 10 important machine learning research papers from 2020. These papers will give you a broad overview of AI research advancements this year. Of course, there are many more breakthrough papers worth reading as well.
We have also published the top 10 lists of key research papers in natural language processing and computer vision . In addition, you can read our premium research summaries , where we feature the top 25 conversational AI research papers introduced recently.
Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries.
If you’d like to skip around, here are the papers we featured:
- A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning
- Efficiently Sampling Functions from Gaussian Process Posteriors
- Dota 2 with Large Scale Deep Reinforcement Learning
- Towards a Human-like Open-Domain Chatbot
- Language Models are Few-Shot Learners
- Beyond Accuracy: Behavioral Testing of NLP models with CheckList
- EfficientDet: Scalable and Efficient Object Detection
- Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
- An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale
- AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Best AI & ML Research Papers 2020
1. a distributed multi-sensor machine learning approach to earthquake early warning , by kévin fauvel, daniel balouek-thomert, diego melgar, pedro silva, anthony simonet, gabriel antoniu, alexandru costan, véronique masson, manish parashar, ivan rodero, and alexandre termier, original abstract .
Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to their propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data, consequently affecting the response time and the robustness of EEW systems.
In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a real-world dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an efficient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometer-only approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength.
Our Summary
The authors claim that traditional Earthquake Early Warning (EEW) systems that are based on seismometers, as well as recently introduced GPS systems, have their disadvantages with regards to predicting large and medium earthquakes respectively. Thus, the researchers suggest approaching an early earthquake prediction problem with machine learning by using the data from seismometers and GPS stations as input data. In particular, they introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, which is specifically tailored for efficient computation on large-scale distributed cyberinfrastructures. The evaluation demonstrates that the DMSEEW system is more accurate than other baseline approaches with regard to real-time earthquake detection.
What’s the core idea of this paper?
- Seismometers have difficulty detecting large earthquakes because of their sensitivity to ground motion velocity.
- GPS stations are ineffective in detecting medium earthquakes, as they are prone to producing lots of noisy data.
- takes sensor-level class predictions from seismometers and GPS stations (i.e. normal activity, medium earthquake, large earthquake);
- aggregates these predictions using a bag-of-words representation and defines a final prediction for the earthquake category.
- Furthermore, they introduce a distributed cyberinfrastructure that can support the processing of high volumes of data in real time and allows the redirection of data to other processing data centers in case of disaster situations.
What’s the key achievement?
- precision – 100% vs. 63.2%;
- recall – 100% vs. 85.7%;
- F1 score – 100% vs. 72.7%.
- precision – 76.7% vs. 70.7%;
- recall – 38.8% vs. 34.1%;
- F1 score – 51.6% vs. 45.0%.
What does the AI community think?
- The paper received an Outstanding Paper award at AAAI 2020 (special track on AI for Social Impact).
What are future research areas?
- Evaluating DMSEEW response time and robustness via simulation of different scenarios in an existing EEW execution platform.
- Evaluating the DMSEEW system on another seismic network.
2. Efficiently Sampling Functions from Gaussian Process Posteriors , by James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model’s success hinges upon its ability to faithfully represent predictive uncertainty. These problems typically exist as parts of larger frameworks, wherein quantities of interest are ultimately defined by integrating over posterior distributions. These quantities are frequently intractable, motivating the use of Monte Carlo methods. Despite substantial progress in scaling up Gaussian processes to large training sets, methods for accurately generating draws from their posterior distributions still scale cubically in the number of test locations. We identify a decomposition of Gaussian processes that naturally lends itself to scalable sampling by separating out the prior from the data. Building off of this factorization, we propose an easy-to-use and general-purpose approach for fast posterior sampling, which seamlessly pairs with sparse approximations to afford scalability both during training and at test time. In a series of experiments designed to test competing sampling schemes’ statistical properties and practical ramifications, we demonstrate how decoupled sample paths accurately represent Gaussian process posteriors at a fraction of the usual cost.
In this paper, the authors explore techniques for efficiently sampling from Gaussian process (GP) posteriors. After investigating the behaviors of naive approaches to sampling and fast approximation strategies using Fourier features, they find that many of these strategies are complementary. They, therefore, introduce an approach that incorporates the best of different sampling approaches. First, they suggest decomposing the posterior as the sum of a prior and an update. Then they combine this idea with techniques from literature on approximate GPs and obtain an easy-to-use general-purpose approach for fast posterior sampling. The experiments demonstrate that decoupled sample paths accurately represent GP posteriors at a much lower cost.
- The introduced approach to sampling functions from GP posteriors centers on the observation that it is possible to implicitly condition Gaussian random variables by combining them with an explicit corrective term.
- The authors translate this intuition to Gaussian processes and suggest decomposing the posterior as the sum of a prior and an update.
- Building on this factorization, the researchers suggest an efficient approach for fast posterior sampling that seamlessly pairs with sparse approximations to achieve scalability both during training and at test time.
- Introducing an easy-to-use and general-purpose approach to sampling from GP posteriors.
- avoid many shortcomings of the alternative sampling strategies;
- accurately represent GP posteriors at a much lower cost; for example, simulation of a well-known model of a biological neuron required only 20 seconds using decoupled sampling, while the iterative approach required 10 hours.
- The paper received an Honorable Mention at ICML 2020.
Where can you get implementation code?
- The authors released the implementation of this paper on GitHub .
3. Dota 2 with Large Scale Deep Reinforcement Learning , by Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław “Psyho” Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, Susan Zhang
On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.
The OpenAI research team demonstrates that modern reinforcement learning techniques can achieve superhuman performance in such a challenging esports game as Dota 2. The challenges of this particular task for the AI system lies in the long time horizons, partial observability, and high dimensionality of observation and action spaces. To tackle this game, the researchers scaled existing RL systems to unprecedented levels with thousands of GPUs utilized for 10 months. The resulting OpenAI Five model was able to defeat the Dota 2 world champions and won 99.4% of over 7000 games played during the multi-day showcase.
- The goal of the introduced OpenAI Five model is to find the policy that maximizes the probability of winning the game against professional human players, which in practice implies maximizing the reward function with some additional signals like characters dying, resources collected, etc.
- While the Dota 2 engine runs at 30 frames per second, the OpenAI Five only acts on every 4th frame.
- At each timestep, the model receives an observation with all the information available to human players (approximated in a set of data arrays) and returns a discrete action , which encodes the desired movement, attack, etc.
- A policy is defined as a function from the history of observations to a probability distribution over actions that are parameterized as an LSTM with ~159M parameters.
- The policy is trained using a variant of advantage actor critic, Proximal Policy Optimization.
- The OpenAI Five model was trained for 180 days spread over 10 months of real time.
- defeated the Dota 2 world champions in a best-of-three match (2–0);
- won 99.4% of over 7000 games during a multi-day online showcase.
- Applying introduced methods to other zero-sum two-team continuous environments.
What are possible business applications?
- Tackling challenging esports games like Dota 2 can be a promising step towards solving advanced real-world problems using reinforcement learning techniques.
4. Towards a Human-like Open-Domain Chatbot , by Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, Quoc V. Le
We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token. We also propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of a human-like multi-turn conversation. Our experiments show strong correlation between perplexity and SSA. The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86% is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots we evaluated.
In contrast to most modern conversational agents, which are highly specialized, the Google research team introduces a chatbot Meena that can chat about virtually anything. It’s built on a large neural network with 2.6B parameters trained on 341 GB of text. The researchers also propose a new human evaluation metric for open-domain chatbots, called Sensibleness and Specificity Average (SSA), which can capture important attributes for human conversation. They demonstrate that this metric correlates highly with perplexity, an automatic metric that is readily available. Thus, the Meena chatbot, which is trained to minimize perplexity, can conduct conversations that are more sensible and specific compared to other chatbots. Particularly, the experiments demonstrate that Meena outperforms existing state-of-the-art chatbots by a large margin in terms of the SSA score (79% vs. 56%) and is closing the gap with human performance (86%).
- Despite recent progress, open-domain chatbots still have significant weaknesses: their responses often do not make sense or are too vague or generic.
- Meena is built on a seq2seq model with Evolved Transformer (ET) that includes 1 ET encoder block and 13 ET decoder blocks.
- The model is trained on multi-turn conversations with the input sequence including all turns of the context (up to 7) and the output sequence being the response.
- making sense,
- being specific.
- The research team discovered that the SSA metric shows high negative correlation (R2 = 0.93) with perplexity, a readily available automatic metric that Meena is trained to minimize.
- Proposing a simple human-evaluation metric for open-domain chatbots.
- The best end-to-end trained Meena model outperforms existing state-of-the-art open-domain chatbots by a large margin, achieving an SSA score of 72% (vs. 56%).
- Furthermore, the full version of Meena, with a filtering mechanism and tuned decoding, further advances the SSA score to 79%, which is not far from the 86% SSA achieved by the average human.
- “Google’s “Meena” chatbot was trained on a full TPUv3 pod (2048 TPU cores) for 30 full days – that’s more than $1,400,000 of compute time to train this chatbot model.” – Elliot Turner, CEO and founder of Hyperia .
- “So I was browsing the results for the new Google chatbot Meena, and they look pretty OK (if boring sometimes). However, every once in a while it enters ‘scary sociopath mode,’ which is, shall we say, sub-optimal” – Graham Neubig, Associate professor at Carnegie Mellon University .
- Lowering the perplexity through improvements in algorithms, architectures, data, and compute.
- Considering other aspects of conversations beyond sensibleness and specificity, such as, for example, personality and factuality.
- Tackling safety and bias in the models.
- further humanizing computer interactions;
- improving foreign language practice;
- making interactive movie and videogame characters relatable.
- Considering the challenges related to safety and bias in the models, the authors haven’t released the Meena model yet. However, they are still evaluating the risks and benefits and may decide otherwise in the coming months.
5. Language Models are Few-Shot Learners , by Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions – something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10× more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3’s few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.
The OpenAI research team draws attention to the fact that the need for a labeled dataset for every new language task limits the applicability of language models. Considering that there is a wide range of possible tasks and it’s often difficult to collect a large labeled training dataset, the researchers suggest an alternative solution, which is scaling up language models to improve task-agnostic few-shot performance. They test their solution by training a 175B-parameter autoregressive language model, called GPT-3 , and evaluating its performance on over two dozen NLP tasks. The evaluation under few-shot learning, one-shot learning, and zero-shot learning demonstrates that GPT-3 achieves promising results and even occasionally outperforms the state of the art achieved by fine-tuned models.
- The GPT-3 model uses the same model and architecture as GPT-2, including the modified initialization, pre-normalization, and reversible tokenization.
- However, in contrast to GPT-2, it uses alternating dense and locally banded sparse attention patterns in the layers of the transformer, as in the Sparse Transformer .
- Few-shot learning , when the model is given a few demonstrations of the task (typically, 10 to 100) at inference time but with no weight updates allowed.
- One-shot learning , when only one demonstration is allowed, together with a natural language description of the task.
- Zero-shot learning , when no demonstrations are allowed and the model has access only to a natural language description of the task.
- On the CoQA benchmark, 81.5 F1 in the zero-shot setting, 84.0 F1 in the one-shot setting, and 85.0 F1 in the few-shot setting, compared to the 90.7 F1 score achieved by fine-tuned SOTA.
- On the TriviaQA benchmark, 64.3% accuracy in the zero-shot setting, 68.0% in the one-shot setting, and 71.2% in the few-shot setting, surpassing the state of the art (68%) by 3.2%.
- On the LAMBADA dataset, 76.2 % accuracy in the zero-shot setting, 72.5% in the one-shot setting, and 86.4% in the few-shot setting, surpassing the state of the art (68%) by 18%.
- The news articles generated by the 175B-parameter GPT-3 model are hard to distinguish from real ones, according to human evaluations (with accuracy barely above the chance level at ~52%).
- “The GPT-3 hype is way too much. It’s impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out.” – Sam Altman, CEO and co-founder of OpenAI .
- “I’m shocked how hard it is to generate text about Muslims from GPT-3 that has nothing to do with violence… or being killed…” – Abubakar Abid, CEO and founder of Gradio .
- “No. GPT-3 fundamentally does not understand the world that it talks about. Increasing corpus further will allow it to generate a more credible pastiche but not fix its fundamental lack of comprehension of the world. Demos of GPT-4 will still require human cherry picking.” – Gary Marcus, CEO and founder of Robust.ai .
- “Extrapolating the spectacular performance of GPT3 into the future suggests that the answer to life, the universe and everything is just 4.398 trillion parameters.” – Geoffrey Hinton, Turing Award winner .
- Improving pre-training sample efficiency.
- Exploring how few-shot learning works.
- Distillation of large models down to a manageable size for real-world applications.
- The model with 175B parameters is hard to apply to real business problems due to its impractical resource requirements, but if the researchers manage to distill this model down to a workable size, it could be applied to a wide range of language tasks, including question answering, dialog agents, and ad copy generation.
- The code itself is not available, but some dataset statistics together with unconditional, unfiltered 2048-token samples from GPT-3 are released on GitHub .
6. Beyond Accuracy: Behavioral Testing of NLP models with CheckList , by Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, Sameer Singh
Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors. Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.
The authors point out the shortcomings of existing approaches to evaluating performance of NLP models. A single aggregate statistic, like accuracy, makes it difficult to estimate where the model is failing and how to fix it. The alternative evaluation approaches usually focus on individual tasks or specific capabilities. To address the lack of comprehensive evaluation approaches, the researchers introduce CheckList , a new evaluation methodology for testing of NLP models. The approach is inspired by principles of behavioral testing in software engineering. Basically, CheckList is a matrix of linguistic capabilities and test types that facilitates test ideation. Multiple user studies demonstrate that CheckList is very effective at discovering actionable bugs, even in extensively tested NLP models.
- The primary approach to the evaluation of models’ generalization capabilities, which is accuracy on held-out data, may lead to performance overestimation, as the held-out data often contains the same biases as the training data. Moreover, this single aggregate statistic doesn’t help much in figuring out where the NLP model is failing and how to fix these bugs.
- The alternative approaches are usually designed for evaluation of specific behaviors on individual tasks and thus, lack comprehensiveness.
- CheckList provides users with a list of linguistic capabilities to be tested, like vocabulary, named entity recognition, and negation.
- Then, to break down potential capability failures into specific behaviors, CheckList suggests different test types , such as prediction invariance or directional expectation tests in case of certain perturbations.
- Potential tests are structured as a matrix, with capabilities as rows and test types as columns.
- The suggested implementation of CheckList also introduces a variety of abstractions to help users generate large numbers of test cases easily.
- Evaluation of state-of-the-art models with CheckList demonstrated that even though some NLP tasks are considered “solved” based on accuracy results, the behavioral testing highlights many areas for improvement.
- helps to identify and test for capabilities not previously considered;
- results in more thorough and comprehensive testing for previously considered capabilities;
- helps to discover many more actionable bugs.
- The paper received the Best Paper Award at ACL 2020, the leading conference in natural language processing.
- CheckList can be used to create more exhaustive testing for a variety of NLP tasks.
- Such comprehensive testing that helps in identifying many actionable bugs is likely to lead to more robust NLP systems.
- The code for testing NLP models with CheckList is available on GitHub .
7. EfficientDet: Scalable and Efficient Object Detection , by Mingxing Tan, Ruoming Pang, Quoc V. Le
Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single-model and single-scale, our EfficientDet-D7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4×–9× smaller and using 13×–42× fewer FLOPs than previous detectors. Code is available on https://github.com/google/automl/tree/master/efficientdet .
The large size of object detection models deters their deployment in real-world applications such as self-driving cars and robotics. To address this problem, the Google Research team introduces two optimizations, namely (1) a weighted bi-directional feature pyramid network (BiFPN) for efficient multi-scale feature fusion and (2) a novel compound scaling method. By combining these optimizations with the EfficientNet backbones, the authors develop a family of object detectors, called EfficientDet . The experiments demonstrate that these object detectors consistently achieve higher accuracy with far fewer parameters and multiply-adds (FLOPs).
- A weighted bi-directional feature pyramid network (BiFPN) for easy and fast multi-scale feature fusion. It learns the importance of different input features and repeatedly applies top-down and bottom-up multi-scale feature fusion.
- A new compound scaling method for simultaneous scaling of the resolution, depth, and width for all backbone, feature network, and box/class prediction networks.
- These optimizations, together with the EfficientNet backbones, allow the development of a new family of object detectors, called EfficientDet .
- the EfficientDet model with 52M parameters gets state-of-the-art 52.2 AP on the COCO test-dev dataset, outperforming the previous best detector with 1.5 AP while being 4× smaller and using 13× fewer FLOPs;
- with simple modifications, the EfficientDet model achieves 81.74% mIOU accuracy, outperforming DeepLabV3+ by 1.7% on Pascal VOC 2012 semantic segmentation with 9.8x fewer FLOPs;
- the EfficientDet models are up to 3× to 8× faster on GPU/CPU than previous detectors.
- The paper was accepted to CVPR 2020, the leading conference in computer vision.
- The high level of interest in the code implementations of this paper makes this research one of the highest-trending papers introduced recently.
- The high accuracy and efficiency of the EfficientDet detectors may enable their application for real-world tasks, including self-driving cars and robotics.
- The authors released the official TensorFlow implementation of EfficientDet.
- The PyTorch implementation of this paper can be found here and here .
8. Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild , by Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi
We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences.
The research group from the University of Oxford studies the problem of learning 3D deformable object categories from single-view RGB images without additional supervision. To decompose the image into depth, albedo, illumination, and viewpoint without direct supervision for these factors, they suggest starting by assuming objects to be symmetric. Then, considering that real-world objects are never fully symmetrical, at least due to variations in pose and illumination, the researchers augment the model by explicitly modeling illumination and predicting a dense map with probabilities that any given pixel has a symmetric counterpart. The experiments demonstrate that the introduced approach achieves better reconstruction results than other unsupervised methods. Moreover, it outperforms the recent state-of-the-art method that leverages keypoint supervision.
- no access to 2D or 3D ground truth information such as keypoints, segmentation, depth maps, or prior knowledge of a 3D model;
- using an unconstrained collection of single-view images without having multiple views of the same instance.
- leveraging symmetry as a geometric cue to constrain the decomposition;
- explicitly modeling illumination and using it as an additional cue for recovering the shape;
- augmenting the model to account for potential lack of symmetry – particularly, predicting a dense map that contains the probability of a given pixel having a symmetric counterpart in the image.
- Qualitative evaluation of the suggested approach demonstrates that it reconstructs 3D faces of humans and cats with high fidelity, containing fine details of the nose, eyes, and mouth.
- The method reconstructs higher-quality shapes compared to other state-of-the-art unsupervised methods, and even outperforms the DepthNet model, which uses 2D keypoint annotations for depth prediction.
- The paper received the Best Paper Award at CVPR 2020, the leading conference in computer vision.
- Reconstructing more complex objects by extending the model to use either multiple canonical views or a different 3D representation, such as a mesh or a voxel map.
- Improving model performance under extreme lighting conditions and for extreme poses.
- The implementation code and demo are available on GitHub .
9. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale , by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer can perform very well on image classification tasks when applied directly to sequences of image patches. When pre-trained on large amounts of data and transferred to multiple recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer attain excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
The authors of this paper show that a pure Transformer can perform very well on image classification tasks. They introduce Vision Transformer (ViT) , which is applied directly to sequences of image patches by analogy with tokens (words) in NLP. When trained on large datasets of 14M–300M images, Vision Transformer approaches or beats state-of-the-art CNN-based models on image recognition tasks. In particular, it achieves an accuracy of 88.36% on ImageNet, 90.77% on ImageNet-ReaL, 94.55% on CIFAR-100, and 77.16% on the VTAB suite of 19 tasks.
- When applying Transformer architecture to images, the authors follow as closely as possible the design of the original Transformer designed for NLP.
- splitting images into fixed-size patches;
- linearly embedding each of them;
- adding position embeddings to the resulting sequence of vectors;
- feeding the patches to a standard Transformer encoder;
- adding an extra learnable ‘classification token’ to the sequence.
- Similarly to Transformers in NLP, Vision Transformer is typically pre-trained on large datasets and fine-tuned to downstream tasks.
- 88.36% on ImageNet;
- 90.77% on ImageNet-ReaL;
- 94.55% on CIFAR-100;
- 97.56% on Oxford-IIIT Pets;
- 99.74% on Oxford Flowers-102;
- 77.16% on the VTAB suite of 19 tasks.
- The paper is trending in the AI research community, as evident from the repository stats on GitHub .
- It is also under review for ICLR 2021 , one of the key conferences in deep learning.
- Applying Vision Transformer to other computer vision tasks, such as detection and segmentation.
- Exploring self-supervised pre-training methods.
- Analyzing the few-shot properties of Vision Transformer.
- Exploring contrastive pre-training.
- Further scaling ViT.
- Thanks to their efficient pre-training and high performance, Transformers may substitute convolutional networks in many computer vision applications, including navigation, automatic inspection, and visual surveillance.
- The PyTorch implementation of Vision Transformer is available on GitHub .
10. AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients , by Juntang Zhuang, Tommy Tang, Sekhar Tatikonda, Nicha Dvornek, Yifan Ding, Xenophon Papademetris, James S. Duncan
Most popular optimizers for deep learning can be broadly categorized as adaptive methods (e.g. Adam) or accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum). For many models such as convolutional neural networks (CNNs), adaptive methods typically converge faster but generalize worse compared to SGD; for complex settings such as generative adversarial networks (GANs), adaptive methods are typically the default because of their stability. We propose AdaBelief to simultaneously achieve three goals: fast convergence as in adaptive methods, good generalization as in SGD, and training stability. The intuition for AdaBelief is to adapt the step size according to the “belief” in the current gradient direction. Viewing the exponential moving average (EMA) of the noisy gradient as the prediction of the gradient at the next time step, if the observed gradient greatly deviates from the prediction, we distrust the current observation and take a small step; if the observed gradient is close to the prediction, we trust it and take a large step. We validate AdaBelief in extensive experiments, showing that it outperforms other methods with fast convergence and high accuracy on image classification and language modeling. Specifically, on ImageNet, AdaBelief achieves comparable accuracy to SGD. Furthermore, in the training of a GAN on Cifar10, AdaBelief demonstrates high stability and improves the quality of generated samples compared to a well-tuned Adam optimizer. Code is available at https://github.com/juntang-zhuang/Adabelief-Optimizer .
The researchers introduce AdaBelief , a new optimizer, which combines the high convergence speed of adaptive optimization methods and good generalization capabilities of accelerated stochastic gradient descent (SGD) schemes. The core idea behind the AdaBelief optimizer is to adapt step size based on the difference between predicted gradient and observed gradient: the step is small if the observed gradient deviates significantly from the prediction, making us distrust this observation, and the step is large when the current observation is close to the prediction, making us believe in this observation. The experiments confirm that AdaBelief combines fast convergence of adaptive methods, good generalizability of the SGD family, and high stability in the training of GANs.
- The idea of the AdaBelief optimizer is to combine the advantages of adaptive optimization methods (e.g., Adam) and accelerated SGD optimizers. Adaptive methods typically converge faster, while SGD optimizers demonstrate better generalization performance.
- If the observed gradient deviates greatly from the prediction, we have a weak belief in this observation and take a small step.
- If the observed gradient is close to the prediction, we have a strong belief in this observation and take a large step.
- fast convergence, like adaptive optimization methods;
- good generalization, like the SGD family;
- training stability in complex settings such as GAN.
- In image classification tasks on CIFAR and ImageNet, AdaBelief demonstrates as fast convergence as Adam and as good generalization as SGD.
- It outperforms other methods in language modeling.
- In the training of a WGAN , AdaBelief significantly improves the quality of generated images compared to Adam.
- The paper was accepted to NeurIPS 2020, the top conference in artificial intelligence.
- It is also trending in the AI research community, as evident from the repository stats on GitHub .
- AdaBelief can boost the development and application of deep learning models as it can be applied to the training of any model that numerically estimates parameter gradient.
- Both PyTorch and Tensorflow implementations are released on GitHub.
If you like these research summaries, you might be also interested in the following articles:
- GPT-3 & Beyond: 10 NLP Research Papers You Should Read
- Novel Computer Vision Research Papers From 2020
- AAAI 2021: Top Research Papers With Business Applications
- ICLR 2021: Key Research Papers
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Top 20 Recent Research Papers on Machine Learning and Deep Learning
Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".
For each paper we also give the year it was published, a Highly Influential Citation count (HIC) and Citation Velocity (CV) measures provided by semanticscholar.org . HIC that presents how publications build upon and relate to each other is result of identifying meaningful citations. CV is the weighted average number of citations per year over the last 3 years. For some references, where CV is zero that means it was blank or not shown by semanticscholar.org .
Most (but not all) of these 20 papers, including the top 8, are on the topic of Deep Learning. However, we see strong diversity - only one author (Yoshua Bengio) has 2 papers, and the papers were published in many different venues: CoRR (3), ECCV (3), IEEE CVPR (3), NIPS (2), ACM Comp Surveys, ICML, IEEE PAMI, IEEE TKDE, Information Fusion, Int. J. on Computers & EE, JMLR, KDD, and Neural Networks. The top two papers have by far the highest citation counts than the rest. Note that the second paper is only published last year. Read (or re-read them) and learn about the latest advances.
- Deep Residual Learning for Image Recognition , by He, K., Ren, S., Sun, J., & Zhang, X. (2016). CoRR, abs/1512.03385. (cited 1436 times, HIC: 137 , CV: 582). Summary: We present a residual learning framework to ease the training of deep neural networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , by Sergey Ioffe, Christian Szegedy (2015) ICML. (cited 946 times, HIC: 56 , CV: 0). Summary: Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
- Large-Scale Video Classification with Convolutional Neural Networks , by Fei-Fei, L., Karpathy, A., Leung, T., Shetty, S., Sukthankar, R., & Toderici, G. (2014). IEEE Conference on Computer Vision and Pattern Recognition (cited 865 times, HIC: 24 , CV: 239) Summary: Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes .
- Microsoft COCO: Common Objects in Context , by Belongie, S.J., Dollár, P., Hays, J., Lin, T., Maire, M., Perona, P., Ramanan, D., & Zitnick, C.L. (2014). ECCV. (cited 830 times, HIC: 78 , CV: 279) Summary: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.
- Learning deep features for scene recognition using places database , by Lapedriza, À., Oliva, A., Torralba, A., Xiao, J., & Zhou, B. (2014). NIPS. (cited 644 times, HIC: 65 , CV: 0) Summary: We introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity.
- Generative adversarial nets , by Bengio, Y., Courville, A.C., Goodfellow, I.J., Mirza, M., Ozair, S., Pouget-Abadie, J., Warde-Farley, D., & Xu, B. (2014) NIPS. (cited 463 times, HIC: 55 , CV: 0) Summary: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
- High-Speed Tracking with Kernelized Correlation Filters , by Batista, J., Caseiro, R., Henriques, J.F., & Martins, P. (2015). CoRR, abs/1404.7584. (cited 439 times, HIC: 43 , CV: 0) Summary: In most modern trackers, to cope with natural image changes, a classifier is typically trained with translated and scaled sample patches. We propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude.
- A Review on Multi-Label Learning Algorithms , by Zhang, M., & Zhou, Z. (2014). IEEE TKDE, (cited 436 times, HIC: 7 , CV: 91) Summary: This paper aims to provide a timely review on multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously.
- How transferable are features in deep neural networks , by Bengio, Y., Clune, J., Lipson, H., & Yosinski, J. (2014) CoRR, abs/1411.1792. (cited 402 times, HIC: 14 , CV: 0) Summary: We experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. Transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task, which was expected, and (2) optimization difficulties related to splitting networks between co-adapted neurons, which was not expected.
- Do we need hundreds of classifiers to solve real world classification problems , by Amorim, D.G., Barro, S., Cernadas, E., & Delgado, M.F. (2014). Journal of Machine Learning Research (cited 387 times, HIC: 3 , CV: 0) Summary: We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods). We use 121 data sets from UCI data base to study the classifier behavior, not dependent on the data set collection. The winners are the random forest (RF) versions implemented in R and accessed via caret) and the SVM with Gaussian kernel implemented in C using LibSVM.
- Knowledge vault: a web-scale approach to probabilistic knowledge fusion , by Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., ... & Zhang, W. (2014, August). In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining ACM. (cited 334 times, HIC: 7 , CV: 107). Summary: We introduce Knowledge Vault, a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories for constructing knowledge bases. We employ supervised machine learning methods for fusing distinct information sources. The Knowledge Vault is substantially bigger than any previously published structured knowledge repository, and features a probabilistic inference system that computes calibrated probabilities of fact correctness.
- Scalable Nearest Neighbor Algorithms for High Dimensional Data , by Lowe, D.G., & Muja, M. (2014). IEEE Trans. Pattern Anal. Mach. Intell., (cited 324 times, HIC: 11 , CV: 69). Summary: We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper.
- Trends in extreme learning machines: a review , by Huang, G., Huang, G., Song, S., & You, K. (2015). Neural Networks, (cited 323 times, HIC: 0 , CV: 0) Summary: We aim to report the current state of the theoretical research and practical advances on Extreme learning machine (ELM). Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics.
- A survey on concept drift adaptation , by Bifet, A., Bouchachia, A., Gama, J., Pechenizkiy, M., & Zliobaite, I. ACM Comput. Surv., 2014 , (cited 314 times, HIC: 4 , CV: 23) Summary: This work aims at providing a comprehensive introduction to the concept drift adaptation that refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time.
- Multi-scale Orderless Pooling of Deep Convolutional Activation Features , by Gong, Y., Guo, R., Lazebnik, S., & Wang, L. (2014). ECCV(cited 293 times, HIC: 23 , CV: 95) Summary: To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN).
- Simultaneous Detection and Segmentation , by Arbeláez, P.A., Girshick, R.B., Hariharan, B., & Malik, J. (2014) ECCV , (cited 286 times, HIC: 23 , CV: 94) Summary: We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS).
- A survey on feature selection methods , by Chandrashekar, G., & Sahin, F. Int. J. on Computers & Electrical Engineering, (cited 279 times, HIC: 1 , CV: 58) Summary: Plenty of feature selection methods are available in literature due to the availability of data with hundreds of variables leading to data with very high dimension.
- One Millisecond Face Alignment with an Ensemble of Regression Trees , by Kazemi, Vahid, and Josephine Sullivan, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2014, (cited 277 times, HIC: 15 , CV: 0) Summary: This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions.
- A survey of multiple classifier systems as hybrid systems , by Corchado, E., Graña, M., & Wozniak, M. (2014). Information Fusion, 16, 3-17. (cited 269 times, HIC: 1 , CV: 22) Summary: A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building.
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Using machine learning to predict high-impact research
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An artificial intelligence framework built by MIT researchers can give an “early-alert” signal for future high-impact technologies, by learning from patterns gleaned from previous scientific publications.
In a retrospective test of its capabilities, DELPHI , short for Dynamic Early-warning by Learning to Predict High Impact, was able to identify all pioneering papers on an experts’ list of key foundational biotechnologies, sometimes as early as the first year after their publication.
James W. Weis, a research affiliate of the MIT Media Lab, and Joseph Jacobson, a professor of media arts and sciences and head of the Media Lab’s Molecular Machines research group, also used DELPHI to highlight 50 recent scientific papers that they predict will be high impact by 2023. Topics covered by the papers include DNA nanorobots used for cancer treatment, high-energy density lithium-oxygen batteries, and chemical synthesis using deep neural networks, among others.
The researchers see DELPHI as a tool that can help humans better leverage funding for scientific research, identifying “diamond in the rough” technologies that might otherwise languish and offering a way for governments, philanthropies, and venture capital firms to more efficiently and productively support science.
“In essence, our algorithm functions by learning patterns from the history of science, and then pattern-matching on new publications to find early signals of high impact,” says Weis. “By tracking the early spread of ideas, we can predict how likely they are to go viral or spread to the broader academic community in a meaningful way.”
The paper has been published in Nature Biotechnology .
Searching for the “diamond in the rough”
The machine learning algorithm developed by Weis and Jacobson takes advantage of the vast amount of digital information that is now available with the exponential growth in scientific publication since the 1980s. But instead of using one-dimensional measures, such as the number of citations, to judge a publication’s impact, DELPHI was trained on a full time-series network of journal article metadata to reveal higher-dimensional patterns in their spread across the scientific ecosystem.
The result is a knowledge graph that contains the connections between nodes representing papers, authors, institutions, and other types of data. The strength and type of the complex connections between these nodes determine their properties, which are used in the framework. “These nodes and edges define a time-based graph that DELPHI uses to learn patterns that are predictive of high future impact,” explains Weis.
Together, these network features are used to predict scientific impact, with papers that fall in the top 5 percent of time-scaled node centrality five years after publication considered the “highly impactful” target set that DELPHI aims to identify. These top 5 percent of papers constitute 35 percent of the total impact in the graph. DELPHI can also use cutoffs of the top 1, 10, and 15 percent of time-scaled node centrality, the authors say.
DELPHI suggests that highly impactful papers spread almost virally outside their disciplines and smaller scientific communities. Two papers can have the same number of citations, but highly impactful papers reach a broader and deeper audience. Low-impact papers, on the other hand, “aren’t really being utilized and leveraged by an expanding group of people,” says Weis.
The framework might be useful in “incentivizing teams of people to work together, even if they don’t already know each other — perhaps by directing funding toward them to come together to work on important multidisciplinary problems,” he adds.
Compared to citation number alone, DELPHI identifies over twice the number of highly impactful papers, including 60 percent of “hidden gems,” or papers that would be missed by a citation threshold.
"Advancing fundamental research is about taking lots of shots on goal and then being able to quickly double down on the best of those ideas,” says Jacobson. “This study was about seeing whether we could do that process in a more scaled way, by using the scientific community as a whole, as embedded in the academic graph, as well as being more inclusive in identifying high-impact research directions."
The researchers were surprised at how early in some cases the “alert signal” of a highly impactful paper shows up using DELPHI. “Within one year of publication we are already identifying hidden gems that will have significant impact later on,” says Weis.
He cautions, however, that DELPHI isn’t exactly predicting the future. “We’re using machine learning to extract and quantify signals that are hidden in the dimensionality and dynamics of the data that already exist.”
Fair, efficient, and effective funding
The hope, the researchers say, is that DELPHI will offer a less-biased way to evaluate a paper’s impact, as other measures such as citations and journal impact factor number can be manipulated, as past studies have shown.
“We hope we can use this to find the most deserving research and researchers, regardless of what institutions they’re affiliated with or how connected they are,” Weis says.
As with all machine learning frameworks, however, designers and users should be alert to bias, he adds. “We need to constantly be aware of potential biases in our data and models. We want DELPHI to help find the best research in a less-biased way — so we need to be careful our models are not learning to predict future impact solely on the basis of sub-optimal metrics like h -Index, author citation count, or institutional affiliation.”
DELPHI could be a powerful tool to help scientific funding become more efficient and effective, and perhaps be used to create new classes of financial products related to science investment.
“The emerging metascience of science funding is pointing toward the need for a portfolio approach to scientific investment,” notes David Lang, executive director of the Experiment Foundation. “Weis and Jacobson have made a significant contribution to that understanding and, more importantly, its implementation with DELPHI.”
It’s something Weis has thought about a lot after his own experiences in launching venture capital funds and laboratory incubation facilities for biotechnology startups.
“I became increasingly cognizant that investors, including myself, were consistently looking for new companies in the same spots and with the same preconceptions,” he says. “There’s a giant wealth of highly-talented people and amazing technology that I started to glimpse, but that is often overlooked. I thought there must be a way to work in this space — and that machine learning could help us find and more effectively realize all this unmined potential.”
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- Published on December 21, 2020
- In Opinions
Top Machine Learning Research Papers Released In 2020
- By Ram Sagar
It has been only two weeks into the last month of the year and arxiv.org, the popular repository for ML research papers has already witnessed close to 600 uploads. This should give one the idea of the pace at which machine learning research is proceeding; however, keeping track of all these research work is almost impossible. Every year, the research that gets maximum noise is usually from companies like Google and Facebook; from top universities like MIT; from research labs and most importantly from the conferences like NeurIPS or ACL.
- CVPR : 1,470 research papers on computer vision accepted from 6,656 valid submissions.
- ICLR : 687 out of 2594 papers made it to ICLR 2020 — a 26.5% acceptance rate.
- ICML : 1088 papers have been accepted from 4990 submissions.
In this article, we have compiled a list of interesting machine learning research work that has made some noise this year.
Natural Language Processing
This is the seminal paper that introduced the most popular ML model of the year — GPT-3. In the paper titled, “Transformers are few shot learners”, the OpenAI team used the same model and architecture as GPT-2 that includes modified initialisation, pre-normalisation, and reversible tokenisation along with alternating dense and locally banded sparse attention patterns in the layers of the transformer. While the GPT-3 model achieved promising results in the zero-shot and one-shot settings, in the few-shot setting, it occasionally surpassed state-of-the-art models.
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Albert: a lite bert.
Usually, increasing model size when pretraining natural language representations often result in improved performance on downstream tasks, but the training times become longer. To address these problems, the authors in their work presented two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. The authors also used a self-supervised loss that focuses on modelling inter-sentence coherence and consistently helped downstream tasks with multi-sentence inputs. According to results, this model established new state-of-the-art results on the GLUE, RACE, and squad benchmarks while having fewer parameters compared to BERT-large.
Check the paper here .
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Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
Microsoft Research, along with the University of Washington and the University of California, in this paper, introduced a model-agnostic and task agnostic methodology for testing NLP models known as CheckList. This is also the winner of the best paper award at the ACL conference this year. It included a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly.
Linformer is a Transformer architecture for tackling the self-attention bottleneck in Transformers. It reduces self-attention to an O(n) operation in both space- and time complexity. It is a new self-attention mechanism which allows the researchers to compute the contextual mapping in linear time and memory complexity with respect to the sequence length.
Read more about the paper here .
Plug and Play Language Models
Plug and Play Language Models ( PPLM ) are a combination of pre-trained language models with one or more simple attribute classifiers. This, in turn, assists in text generation without any further training. According to the authors, model samples demonstrated control over sentiment styles, and extensive automated and human-annotated evaluations showed attribute alignment and fluency.
Reformer
The researchers at Google, in this paper , introduced Reformer. This work showcased that the architecture of a Transformer can be executed efficiently on long sequences and with small memory. The authors believe that the ability to handle long sequences opens the way for the use of the Reformer on many generative tasks. In addition to generating very long coherent text, the Reformer can bring the power of Transformer models to other domains like time-series forecasting, music, image and video generation.
To overcome the limitations of sparse transformers, Google, in another paper, introduced Performer which uses an efficient (linear) generalised attention framework and has the potential to directly impact research on biological sequence analysis and more. The authors stated that modern bioinformatics could immensely benefit from faster, more accurate language models, for development of new nanoparticle vaccines.
Check paper here .
Computer Vision
An image is worth 16x16 words.
Recent conversation with a friend: @ilyasut : what's your take on https://t.co/fqVhQNaBWQ ? @OriolVinyalsML : my take is: farewell convolutions : ) pic.twitter.com/9PEvxmWvO4 — Oriol Vinyals (@OriolVinyalsML) October 3, 2020
The irony here is that one of the popular language models, Transformers have been made to do computer vision tasks. In this paper , the authors claimed that the vision transformer could go toe-to-toe with the state-of-the-art models on image recognition benchmarks, reaching accuracies as high as 88.36% on ImageNet and 94.55% on CIFAR-100. For this, the vision transformer receives input as a one-dimensional sequence of token embeddings. The image is then reshaped into a sequence of flattened 2D patches. The transformers in this work use constant widths through all of its layers.
Unsupervised Learning of Probably Symmetric Deformable 3D Objects
Winner of the CVPR best paper award, in this work, the authors proposed a method to learn 3D deformable object categories from raw single-view images, without external supervision. This method uses an autoencoder that factored each input image into depth, albedo, viewpoint and illumination. The authors showcased that reasoning about illumination can be used to exploit the underlying object symmetry even if the appearance is not symmetric due to shading.
Generative Pretraining from Pixels
In this paper , OpenAI researchers examined whether similar models can learn useful representations for images. For this, the researchers trained a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, the researchers found that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, it achieved 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full fine-tuning and matching the top supervised pre-trained models. An even larger model, trained on a mixture of ImageNet and web images, is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of their features.
Reinforcement Learning
Deep reinforcement learning and its neuroscientific implications.
In this paper, the authors provided a high-level introduction to deep RL , discussed some of its initial applications to neuroscience, and surveyed its wider implications for research on brain and behaviour and concluded with a list of opportunities for next-stage research. Although DeepRL seems to be promising, the authors wrote that it is still a work in progress and its implications in neuroscience should be looked at as a great opportunity. For instance, deep RL provides an agent-based framework for studying the way that reward shapes representation, and how representation, in turn, shapes learning and decision making — two issues which together span a large swath of what is most central to neuroscience.
Dopamine-based Reinforcement Learning
Why humans doing certain things are often linked to dopamine , a hormone that acts as the reward system (think: the likes on your Instagram page). So, keeping this fact in hindsight, DeepMind with the help of Harvard labs, analysed dopamine cells in mice and recorded how the mice received rewards while they learned a task. They then checked these recordings for consistency in the activity of the dopamine neurons with standard temporal difference algorithms. This paper proposed an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning. The authors hypothesised that the brain represents possible future rewards not as a single mean but as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel.
Lottery Tickets In Reinforcement Learning & NLP
In this paper, the authors bridged natural language processing (NLP) and reinforcement learning (RL). They examined both recurrent LSTM models and large-scale Transformer models for NLP and discrete-action space tasks for RL. The results suggested that the lottery ticket hypothesis is not restricted to supervised learning of natural images, but rather represents a broader phenomenon in deep neural networks.
What Can Learned Intrinsic Rewards Capture
In this paper, the authors explored if the reward function itself can be a good locus of learned knowledge. They proposed a scalable framework for learning useful intrinsic reward functions across multiple lifetimes of experience and showed that it is feasible to learn and capture knowledge about long-term exploration and exploitation into a reward function.
Miscellaneous
Automl- zero.
The progress of AutoML has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks, or similarly restrictive search spaces. In this paper , the authors showed that AutoML could go further with AutoML Zero, that automatically discovers complete machine learning algorithms just using basic mathematical operations as building blocks. The researchers demonstrated this by introducing a novel framework that significantly reduced human bias through a generic search space.
Rethinking Batch Normalization for Meta-Learning
Batch normalization is an essential component of meta-learning pipelines. However, there are several challenges. So, in this paper, the authors evaluated a range of approaches to batch normalization for meta-learning scenarios and developed a novel approach — TaskNorm. Experiments demonstrated that the choice of batch normalization has a dramatic effect on both classification accuracy and training time for both gradient-based and gradient-free meta-learning approaches. The TaskNorm has been found to be consistently improving the performance.
Meta-Learning without Memorisation
Meta-learning algorithms need meta-training tasks to be mutually exclusive, such that no single model can solve all of the tasks at once. In this paper, the authors designed a meta-regularisation objective using information theory that successfully uses data from non-mutually-exclusive tasks to efficiently adapt to novel tasks.
Understanding the Effectiveness of MAML
Model Agnostic Meta-Learning (MAML) consists of optimisation loops, from which the inner loop can efficiently learn new tasks. In this paper, the authors demonstrated that feature reuse is the dominant factor and led to ANIL (Almost No Inner Loop) algorithm — a simplification of MAML where the inner loop is removed for all but the (task-specific) head of the underlying neural network.
Your Classifier is Secretly an Energy-Based Model
This paper proposed attempts to reinterpret a standard discriminative classifier as an energy-based model. In this setting, wrote the authors, the standard class probabilities can be easily computed. They demonstrated that energy-based training of the joint distribution improves calibration, robustness, handout-of-distribution detection while also enabling the proposed model to generate samples rivalling the quality of recent GAN approaches. This work improves upon the recently proposed techniques for scaling up the training of energy-based models. It has also been the first to achieve performance rivalling the state-of-the-art in both generative and discriminative learning within one hybrid model.
Reverse-Engineering Deep ReLU Networks
This paper investigated the commonly assumed notion that neural networks cannot be recovered from its outputs, as they depend on its parameters in a highly nonlinear way. The authors claimed that by observing only its output, one could identify the architecture, weights, and biases of an unknown deep ReLU network. By dissecting the set of region boundaries into components associated with particular neurons, the researchers showed that it is possible to recover the weights of neurons and their arrangement within the network.
(Note: The list is in no particular order and is a compilation based on the reputation of the publishers, reception to these research work in popular forums and feedback of the experts on social media. If you think we have missed any exceptional research work, please comment below)
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Announcing the NeurIPS 2022 Awards
Sahra Ghalebikesabi (Comms Chair 2022) 2022 Conference
by Alekh Agarwal, Alice Oh, Danielle Belgrave, Kyunghyun Cho, Deepti Ghadiyaram, Joaquin Vanschoren
We are excited to announce the award-winning papers for NeurIPS 2022! The three categories of awards are Outstanding Main Track Papers, Outstanding Datasets and Benchmark Track papers, and the Test of Time paper. We thank the awards committee for the main track, Anima Anandkumar, Phil Blunsom, Naila Murray, Devi Parikh, Rajesh Ranganath, and Tong Zhang. For the Datasets and Benchmarks track, we thank Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Neil Lawrence, Olga Russakovsky, and Serena Yeung.
Congratulations to all authors!
Outstanding Papers
- Is Out-of-distribution Detection Learnable? by Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu This work provides a theoretical study of out-of-distribution (OOD) detection, focusing on the conditions under which such models are learnable. The work uses probably approximately correct (PAC) learning theory to show that OOD detection models are PAC learnable only for some conditions of the space of data distributions and the space of prediction models. It provides 3 concrete impossibility theorems, which can be easily applied to determine the feasibility of OOD detection in practical settings, and which was used in this work to provide a theoretical grounding for existing OOD detection approaches. This work also raises new theoretical questions, for example, about the learnability of near-OOD detection. As such, it has the potential for broad theoretical and practical impact in this important research area. Tues Nov 29 — Poster Session 1
- Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding by Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Raphael Gontijo-Lopes, Tim Salimans, Jonathan Ho, David J Fleet, Mohammad Norouzi High quality generative models of images based on Diffusion Process are having a huge impact both within and beyond machine learning. This work represents one of the state of the art of such models, but also innovates in demonstrating the effective combination of an independently trained large language model with an image decoder at scale. This inherently practical decoupling is likely to be a dominant paradigm for large scale text to image models. The results are impressive and of interest to a broad audience. Thurs Dec 1 — Poster Session 5
- Elucidating the Design Space of Diffusion-Based Generative Models by Tero Karras, Miika Aittala, Timo Aila, Samuli Laine This paper is an excellent demonstration of how a well thought through survey, that seeks not just to list but to organise prior research into a coherent common framework, can provide insights that then lead to new modelling improvements. In this case the focus on this paper are generative models of images that incoporate some form of Diffusion Process, which have become extremely popular recently despite the difficulties of training such models. This paper is likely to be an important contribution in the evolution of both the understanding and implementation of Diffusion Process based models. Wed Dec 7 — Featured Papers Panels 3B
- ProcTHOR: Large-Scale Embodied AI Using Procedural Generation by Matt Deitke, Eli VanderBilt, Alvaro Herrasti, Luca Weihs, Kiana Ehsani, Jordi Salvador, Winson Han, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi This work provides a framework for training embodied AI agents on large quantities of data, creating the potential for such agents to benefit from scaling, as language and image generation models have. The core of the framework is an engine for building procedurally-generated, physics-enabled environments with which agents can interact. This engine, in combination with provided digital assets and environmental controls, allows for generating a combinatorially large number of diverse environments. The authors demonstrate that this framework can be used to train SoTA models for several embodied AI tasks. The framework and code used in this work will be open-sourced, providing a valuable asset for the research community. Wed Nov 30 — Poster Session 3
- Using natural language and program abstractions to instill human inductive biases in machines by Sreejan Kumar , Carlos G Correa, Ishita Dasgupta, Raja Marjieh, Michael Hu, Robert D. Hawkins, Jonathan Cohen, Nathaniel Daw, Karthik R Narasimhan, Thomas L. Griffiths Co-training on program abstractions and natural language enables incorporating human biases into learning. This is a clean approach to incorporating human biases but also be robust with program abstractions. Thurs Dec 1 — Poster Session 6
- A Neural Corpus Indexer for Document Retrieval by Yujing Wang , Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Hao Sun, Qi Chen , Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, Xing Xie, Hao Sun, Weiwei Deng, Qi Zhang, Mao Yang This work proposes a neural indexer that takes as input a query and outputs, via a decoder combined with beam search, a list of IDs corresponding to relevant documents in the index. It joins a small but growing line of research that departs from the dominant high recall-sparse retrieval paradigm. Notably, this new paradigm allows for gradient-based optimization of the indexer for target applications using standard deep learning algorithms and frameworks. The proposed approach introduces architectural and training choices that result in significant improvements compared to prior work, demonstrating the promise of neural indexers as a viable alternative. The paper is well-written and discusses the limitations and open questions following from this work, which can serve as inspiration for future research. Thurs Dec 1 — Poster Session 5
- High-dimensional limit theorems for SGD: Effective dynamics and critical scaling by Gerard Ben Arous , Reza Gheissari, Aukosh Jagannath This work studies the scaling limits of SGD with constant step-size in the high-dimensional regime. It shows how complex SGD can be if the step size is large. Characterizing the nature of SDE and comparing it to the ODE when the step size is small gives insights into the nonconvex optimization landscape.
- Gradient Descent: The Ultimate Optimizer by Kartik Chandra , Audrey Xie, Jonathan Ragan-Kelley, Erik Meijer This paper reduces sensitivity to hyperparameters in gradient descent by developing a method to optimize with respect to hyperparameters and recursively optimize *hyper*-hyperparameters. Since gradient descent is everywhere, the potential impact is tremendous. Wed Nov 30 — Poster Session 4
- Riemannian Score-Based Generative Modelling by Valentin De Bortoli , Emile Mathieu, Michael John Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet The paper generalizes score-based generative model (SGM) from Euclidean space to Riemannian manifolds by identifying major components that contribute to the success of SGMs. The method is both a novel and technically useful contribution. Wed Nov 30 — Poster Session 4
- Gradient Estimation with Discrete Stein Operators by Jiaxin Shi , Yuhao Zhou, Jessica Hwang, Michalis Titsias, Lester Mackey This paper considers gradient estimation when the distribution is discrete. Most common gradient estimators suffer from excessive variance. To improve the quality of gradient estimation, they introduce a variance reduction technique based on Stein operators for discrete distributions. Even though Stein operator is classical, this work provides a nice interpretation of it for gradient estimation and also shows practical improvement in experiments. Tues Nov 29 — Poster Session 1
- An empirical analysis of compute-optimal large language model training by Jordan Hoffmann , Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katherine Millican, Geo rge van den Driessche , Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich E l sen , Oriol Vinyals, Jack William Rae, Laurent Sifre The work asks “Given a fixed FLOPs budget, how should one trade-off model size and the number of training tokens?”. The work models this trade off, makes a prediction based on this model, and trains a model corresponding to that prediction. The resultant model, that is significantly smaller but is trained on significantly more tokens, outperforms its counterpart, while also being more practical to use downstream due to its smaller size. All in all, this work sheds new light on the way the community thinks about scale in the context of language models, which may be useful in other domains of AI as well. Wed Nov 30 — Poster Session 4
- Beyond neural scaling laws: beating power law scaling via data pruning by Ben Sorscher , Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari S. Morcos The importance of high quality data in order to achieve good results in machine learning is well known. Recent work on scaling laws has treated data quality as uniform and focussed on the relationship between computation and data. This work renews our focus on the importance of selecting high quality data as a means to achieve optimal scaling. It does so through a nicely designed analytic investigation that develops a theoretical model of the impact of data quality in concert with empirical instantiation of a range of data filtering metrics on ImageNet. This work is both insightful and timely and will shape the debate about the tradeoffs in the many dimensions of scale in machine learning. Wed Nov 30 — Poster Session 3
- On-Demand Sampling: Learning Optimally from Multiple Distributions by Nika Haghtalab , Michael Jordan, Eric Zhao This paper studies multiple distribution learning using techniques from stochastic zero-sum games. This technique leads to very interesting theoretical results for a class of problems with near optimal results. Wed Nov 30 — Poster Session 3
Outstanding Datasets and Benchmarks Papers
- LAION-5B: An open large-scale dataset for training next generation image-text models by Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, Jenia Jitsev Studying the training and capabilities of language-vision architectures, such as CLIP and DALL-E, requires datasets containing billions of image-text pairs. Until now, no datasets of this size have been made openly available for the broader research community. This work presents LAION-5B, a dataset consisting of 5.85 billion CLIP-filtered image-text pairs, aimed at democratizing research on large-scale multi-modal models. Moreover, the authors use this data to successfully replicate foundational models such as CLIP, GLIDE and Stable Diffusion, provide several nearest neighbor indices, as well as an improved web-interface, and detection scores for watermark, NSFW, and toxic content detection. Wed Nov 30 — Poster Session 4
- MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge by Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar Autonomous agents have made great strides in specialist domains like Atari games and Go, but typically fail to generalize across a wide spectrum of tasks and capabilities. This work introduces MineDojo, a new framework built on the popular Minecraft game that features a simulation suite with thousands of diverse open-ended tasks and an internet-scale knowledge base with Minecraft videos, tutorials, wiki pages, and forum discussions. It also proposes a novel agent learning algorithm that is able to solve a variety of open-ended tasks specified in free-form language. It provides an open-source simulation suite, knowledge bases, algorithm implementation, and pretrained models to promote research on generally capable embodied agents. Tue Nov 29 — Poster Session 2
Test of Time Award
This year, following the usual practice, we chose a NeurIPS paper from 10 years ago, and “ ImageNet Classification with Deep Convolutional Neural Networks ” by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, aka “AlexNet paper” was unanimously selected by the Program Chairs. In 2012, it was presented as the first CNN trained on the ImageNet Challenge, far surpassing the state-of-the-art at the time, and since then it has made a huge impact on the machine learning community. Geoff will be giving an invited talk on this and more recent research on Thursday, Dec. 1, at 2:30 pm. https://neurips.cc/Conferences/2022/ScheduleMultitrack?event=55869
We again congratulate the award winners and thank the award committee members and the reviewers, ACs, and SACs for nominating the papers. We are looking forward to hearing from the authors of these and all other NeurIPS 2022 papers in New Orleans and on our virtual platform.
Alekh Agarwal, Alice Oh, Danielle Belgrave, Kyunghyun Cho
NeurIPS 2022 Program Chairs
Deepti Ghadiyaram, Joaquin Vanschoren
NeurIPS 2022 Datasets and Benchmark Chairs
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Getting Started with Research Papers on Machine Learning: What to Read & How
Machine learning
The food or grocery segment is one area where Machine Learning has left an indelible mark. Up to 40% of a grocer’s revenue comes from sales of fresh produce. Therefore, maintaining product quality is very important. But that is easier said than done.
Grocers are dependent on their supply chains and consumers. Keeping their shelves stocked and their products fresh is a difficult situation for them.
But with machine learning grocers already know the secret to smarter fresh-food replenishment. They can train ML programs on historical datasets and input data about promotions and store hours as well. Then use the analyses to gauge how much of each product to order and display.
ML systems can also collect information about weather forecasts, public holidays, order quantity parameters, and other contextual information.
Grocers or store-owners can then issue a recommended order every 24 hours so that the grocer always has the appropriate products in the appropriate amounts in stock.
Research Papers on Machine Learning Algorithms
Research papers on machine learning algorithms
Research Papers on Machine Learning have questioned which machine learning algorithm and what underlying model structure to use has been based on time-consuming investigations and research by human experts.
It has been found out that the right way to select the best algorithms and the most appropriate model architecture, with the correct hyper-parameters, is through trial and error.
Meta-Learning, as it has evolved through the latest research papers on machine learning. It is a concept where exploration of algorithms and model structures take place using machine learning methods.
For us, learning happens at multiple scales. Our brains are born with the ability to learn new concepts and tasks. Similarly, research papers in Machine Learning show that in Meta-Learning or Learning to Learn, there is a hierarchical application of AI algorithms.
This includes first learning which is the best network architecture, and what optimization algorithms and hyper-parameters are most appropriate for the model that has been selected.
The model that has been selected through this process refines the most mundane of tasks. The research has already achieved remarkable results and with the use of different optimization techniques. Evolutionary Strategies is perhaps the best example of this.
Evolutionary strategies in machine learning
However, with a Meta- Reinforcement Learning Algorithm, the objective is to learn the working behind Reinforcement Learning agent that includes both the Reinforcement Learning algorithm and the policy.
Pieter Abbeel gave an explanation for this at the Meta-Learning Symposium held during NIPS 2017. This was also one of the highest rated research papers on Machine Learning.
In one of the several research papers in Machine Learning, Oriol Vinyals states that humans are capable of learning new concepts with minimal supervision. In a Deep Learning network, there is a requirement of huge amount of labelled training data because neural networks are still not able to recognize a new object that they have only seen once or twice.
However, more recent researches on machine learning have shown that the application of model-based, or metric-based, or optimization-based Meta-Learning approaches to define network architectures that can learn from just a few data examples.
Moreover, the latest research papers on machine learning, i.e., on One-Shot Learning by Vinyals shows significant improvements have taken place over previous baseline one-shot accuracy for video and language tasks.
This approach uses a model that learns a classifier based on an attention kernel to map a small labelled support set and an unlabelled example to its corresponding label
Again, for Reinforcement Learning applications, One-Shot Imitation Learning brings out the possibility of learning from just a few demonstrations of a given task. It is possible to generalize to new instances of the same task by applying a Meta-Learning approach to train robust policies.
Several existing Reinforcement Learning (RL) systems, today rely on simulations to explore the solution space and solve complex problems. These include systems based on Self-Play for gaming applications.
Self-Play is an essential part of the algorithms used by Google\DeepMind in AlphaGo. In the more recent AlphaGo Zero reinforcement learning systems. These are some of the breakthrough approaches that have defeated the world champion at the ancient Chinese game of Go.
Research papers on machine learning: simulation-based learning
Thus, it is interesting to note that the newer AlphaGo Zero system has achieved a significant step forward. The training of AlphaGo Zero system was entirely by Self-Play RL starting from a completely random play. It received no human data or supervision input. The system is effectively self-learning.
Therefore, simulation for Reinforcement Learning training has also been used in Imagination Augmented RL algorithms – the recent Imagination-Augmented Agents (I2A) approach improves on the original model-based RL algorithms by combining both model-free and model-based policy rollouts.
Thus, this approach allows the policy improvement & has resulted in a significant improvement in performance.
Wasserstein research paper on Auto-Encoders shows how Autoencoders, which are neural networks, are used for dimensionality reduction. Autoencoders are more popularly used for generative learning models. Variational autoencoder (VAE) is largely used in applications in image and text recognition space.
Moreover, researchers from Max Planck Institute for Intelligent Systems, Germany, in collaboration with scientists from Google Brain have come up with the Wasserstein Auto encoder (WAE). It is capable of utilizing Wasserstein distance in any generative model.
Their aim was to reduce optimal transport cost function in the model distribution.
Thus, after testing, WAE proved to be more functional. It provided a more stable solution than other auto encoders such as VAE with lesser architectural complexity.
Research papers on machine learning: the wasserstein auto-encoder
Authors of the paper on Ultra-strong machine learning comprehensibility of programs learned with ILP are among the most widely read research papers on machine learning algorithms . They introduced an operational definition for comprehensibility of logic programs. They conducted human trials to determine how properties of a program affect its ease of comprehension.
As a matter of fact, Scholars have used two sets of experiments testing human comprehensibility of logic programs. In the first experiment, they have tested human comprehensibility with and without predicate invention.
Ultra-strong machine learning comprehensibility of programs learned with ilp
Thus, in the second experiment, researchers have directly tested whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in human trials.
The results show that participants were not able to learn the relational concept on their own from a set of examples. They were able to apply the relational definition provided by the ILP system correctly.
Moreover, this implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. The scholars are of opinion that improved understanding of this class could have potential relevance to contexts involving human learning, teaching, and verbal interaction.
While all of the aforementioned papers present a unique perspective in the advancements in machine learning, you must develop your own thoughts on a hot topic and publish it.
The novel methods mentioned in these research papers in machine learning provide diverse avenues for ML research. As a Machine Learning and artificial intelligence enthusiasts, you can gain a lot when it comes to the latest techniques developed in research.
Thus, as a researcher, Machine Learning looks promising as a career option. You may go for a course in MOOC or take up online courses like the John Hopkins Data Science specialization.
Thus, participating in Kaggle or other online machine learning competitions will also help you gain experience. Attending local meetups or academic conferences is always a fruitful way to learn.
You may also enroll in a Data Analytics course for more lucrative career options in Data Science . Moreover, Industry-relevant curriculums, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya. Need experts for creating a killer resume that stands out in the crowd?
Careers in machine learning
Thus, for a rewarding career in Machine Learning , one must stay up to date with any up and coming changes. This also means staying abreast of the latest developments for tools, theory and algorithms.
Furthermore, online communities are great places to know of these changes. Also, read a lot. Read articles on Google Map-Reduce, Google File System, Google Big Table, and The Unreasonable Effectiveness of Data. You will get plenty of free Machine Learning books online. Practice problems, coding competitions, and hackathons are a great way to hone your skills.
Moreover, try finding answers to questions at the end of every research paper on Machine Learning. In addition to research papers in machine learning, subscribe to Machine Learning newsletters or join Machine Learning communities. The latter is better as it helps you gain knowledge through practical implementation of Machine Learning.
Therefore, to build a promising career in Machine Learning, join the Machine Learning Course .
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Machine Learning with Applications
About the journal, aims & scope.
Machine Learning with Applications (MLWA) is a peer reviewed, open access journal focused on research related to machine learning . The journal encompasses all aspects of research and development in ML, including but not limited to data mining, computer vision, natural language processing (NLP), …
Article Publishing Charge for open access
* This discount is valid for all authors who wish to publish open access and submit their article by 30 April 2023
Editor-in-Chief
Prof. dr. binshan lin, phd.
Louisiana State University in Shreveport, Shreveport, Louisiana, United States of America
Latest published
Articles in press, most downloaded, most popular, graph-powered learning methods in the internet of things: a survey, a comparative performance analysis of intelligence-based algorithms for optimizing competitive facility location problems, a bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems, portfolio optimization based on neural networks sensitivities from assets dynamics respect common drivers, deep generative modelling of aircraft trajectories in terminal maneuvering areas, a hybrid attention mechanism for multi-target entity relation extraction using graph neural networks, behavioral recommendation engine driven by only non-identifiable user data, implementing associative memories by echo state network for the applications of natural language processing, more from machine learning with applications, announcements, charting the growth of machine learning’s hottest topic: graph neural networks, guidelines for submitting proposals for journal special issues, calls for papers, emerging integrated large language models and training paradigms, chatgpt and similar ai tools: their potential and challenges in today’s world, special issues and article collections, machine learning in finance, partner journals, expert systems with applications.
Expert Systems With Applications is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry , government , and universities worldwide. The thrust of the journal is to publish papers dealing with the design, development, …
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For developers, how to write a good research paper in the machine learning area.
A research paper on machine learning refers to the proper technical documentation that explains any fundamental theory, topic survey, or proof of concept using a mathematical model or practical implementation. It demands hours of study and effort to lay out all the information ideally that addresses the topic in a presentable manner.
The reviewers of the research paper utilize thumb rules, such as replicability of results, availability of code, and others, to analyze its worth. Additionally, the acceptance guidelines from all the prestigious journals and conferences like ICLR, ICML, NeurIPS, and others are quite strict. After so much skimming of the research paper, only a few lucky ones get selected and the rest are all discarded.
These few high-valued papers get published or applauded by the top researchers of the community and they get into practical applications.
Thus, it is important to know the ins and outs of how to write research paper in machine learning. In this article, we will help you with expert advice on how you can ace your research paper in machine learning.
Table of Contents
- 1. What makes an excellent research paper on machine learning?
- 2. What are the important parts of a research paper?
- 3. Types of machine learning papers you can write
- 4. How to write a successful research paper in machine learning?
- 5. How to submit your machine learning research papers?
- 6. How are machine learning papers assessed?
- 7. Do’s and don't of writing research paper
- 8. Conclusion
What makes an excellent research paper on machine learning?
An excellent machine learning paper is based on good research that is transparent and reproducible. It should be replicable in nature so that the study's findings can be tested by other researchers.
Such papers demand research with a completely new architecture, algorithm, or fundamentals. Include the goals of your research and categorize your paper in terms of some familiar class like a computational model of human learning, a formal analysis, application of any established methods, a description of any new learning algorithm, and others.
Further, ensure that you bring together various evidence, views, and facts about the topic that you are targeting in machine learning. You can derive your information from different interviews, articles, and books to verify the facts included in your research paper.
The four major characteristics that the writer of a machine learning research paper should consider are its length, format, style, and sources.
Additionally, including an abstract to your research paper will bring your machine learning paper into a nutshell from its introduction to the conclusion.
What are the important parts of a research paper?
An excellent research paper is drafted in a formal structure that includes several sections maintaining the flow of the content. It is important to ensure that the readers can quickly find the information they are looking for in your research paper.
Here’s a complete list of everything a research paper should include.
- Introduction
- Methodology
- Discussion and conclusion
These are some of the standard sections that is available is almost every research paper. However, there can be additional sections based on the topic you choose to write on, such as a dedicated space for the related research papers on machine learning to the author’s work.
Types of machine learning papers you can write
The initial step toward writing an excellent machine learning research paper is to select your targeted category. The below-given image will clear your thoughts on the same.
1. Survey paper without implementation
This paper category includes an excessive survey for any machine learning domain. For example, if someone wants to write a research paper on healthcare and machine learning, there will be tons of research already being carried out. To summarize that work in a single paper by finding some interesting facts can be enough to start with survey paper writing.
The following are excellent websites to check for the latest research papers.
- Google Scholar
- DBLP - computer science bibliography
- WorldWideScience
- Science.Gov
- Virtual Learning Resources Center
You can download a research paper on machine learning from the sites mentioned above, and then you can take any particular application or algorithm and check for advancement in it. Finally, prepare the summarized table of all the research held in your selected area with proper citation, its merits, and demerits.
2. Survey Paper With Implementation
If you wish to write a survey paper with implementation, you should select a topic and get the dataset for that domain. Following are the websites to get a free dataset.
- Google Dataset Search
- Open Data Portal
- AWS Open Data
- Academic Torrents
For example, using various machine learning algorithms, you can select the topic as employee attrition prediction. Next, you can datasets available for public use, apply supervised or unsupervised machine learning algorithms, and check the accuracy. Finally, show the comparative table of all five or six algorithms you are using for that dataset and conclude the best algorithm for your chosen problems.
3. Paper with just proof of concept
This category of paper requires in-depth knowledge of the selected area. Here, you must understand any available machine learning or deep learning algorithm and optimize it by modifying it or analyzing it mathematically. This paper showcases the brief, logical, and technical proof of the proposed new architecture or algorithm.
4. Developing new machine learning algorithms
Machine learning is still an emerging field. However, there are many application areas of machine learning algorithms like agriculture, health, social media, computer vision, image processing, NLP , sentimental analysis, recommender system, prediction, business analytics, and almost all the fields can directly or indirectly use machine learning in one or another way.
Any machine learning algorithm developed for one application may not work with the same efficiency on another application. Most of the algorithms are application-specific. So, there is always a scope to design a new algorithm for the application. For example, if you wish to apply machine learning for mangrove classification from satellite images, you need to modify any available algorithm that is good for camera-captured images and not satellite images. So it gives scope to create or modify the available algorithm.
5. Developing new architecture
IoT, or the Internet of Things, is an emerging field in the artificial intelligence area. As described in the previous point, machine learning can be applied in almost all areas. So, whenever you wish to include ML in IoT, it gives rise to new IoT+ML architecture. Such type of paper includes newly developed architecture for any technology. Green IoT, Privacy-Preserving ML, IoTML, Healthcare, ML, and more, are areas where there is huge research scope for new or modified architecture.
6. Comparison of various machine learning algorithms
This category of paper sounds more like a survey paper. The paper title for such category includes, “House price prediction: Survey of various machine learning algorithms.” Thus, such a paper includes one problem domain, and all the possible implementations which have already been done are documented using proper citations.
The main novelty of this type of paper lies in the summarized table, which includes algorithms, methods, merits, and demerits of using that algorithm for a given problem domain.
7. Analysis of any manually collected data
This kind of paper is generally preferred in MBA programs. Here researchers send Google forms or any physical questionaries’ to the end-users. The data is collected as per the user experience. Such collected data is then applied to any machine learning model for classification or prediction. Sometimes it can also be used to perform regression analysis . It can also be used for any data collected for business analytics. For example, searching buyers’ buying patterns or churn prediction.
8. Applying ML algorithms for prediction or classification
It is a purely implementation-based category. The first step here will be to define the problem statement, then select the properly suitable dataset for it, and divide the data into training and testing sets. Then assign the target variable in the case of supervised learning. Fit the appropriate machine learning model. Evaluate the result.
To sum up, the points mentioned above, research paper writing is not a skill that can be acquired in a few minutes, but it is a skill you acquire with more and more practice. To write a good research paper, one should be very clear with the objectives. Then, perform the implementation parts and demonstrate the results fruitfully.
How to write a successful research paper in machine learning?
1. Write as if your reader knows nothing
An average reader is not aware of the importance of your topic. You need to formulate clear thoughts and back up your information with credible sources. Spend enough time on your research and make the reader aware of your topic in the introduction section of your work.
Additionally, you need to bear at least four kinds of readers in mind while writing your research paper on machine learning.
Professionals of your research field: The people in the same research field as yours will know all the relevant terms and related work of your research. They will be a few in number and are less likely to be your peers.
Professionals in closely related research areas: Such people would not be aware of your research or the specific problems you are addressing in your research. But they do have a general understanding of the wider research area you are targeting. So it is important to include an aspect from their perspective to keep them connected till the conclusion of your research paper.
Supervisor: Your supervisor would already know what and why you are doing in your research paper. We recommend that you don’t write a research paper with your supervisor as a reader in your mind.
Professionals from remote areas: The biggest portion of your readers are the people from remotely related research areas. This group would include some of the reviewers or the people who aren’t aware of the importance of your research or methods. We recommend you not explain the same to them and continue writing a research paper considering a basic understanding of the topic in your readers' minds.
2. Write when your results are ready
It is important to have the results on the table before you start writing your machine learning research paper. However, you can write the introduction part as early as possible even before having your results analyzed. This exception will help you get a clear picture of your deep learning papers and identify the relevant work.
Many authors of the machine learning research paper may question the ticking clock towards the deadline. But it is important to know the complete story from the introduction to the conclusion before writing it down. We recommend you get the results of your research first, run an analysis of them, and then move on to writing all about it in your research paper.
3. Review your paper like a critic
There are some things that, as a research paper writer, you should be accustomed to. We have listed them below for you.
- Be aware of the limitations of your research. Make a list of all of them.
- Search for any weaknesses in the paper. If they can be fixed, resolve them or else describe the limits of what you did instead of giving an excuse.
- Proofread your research paper to its bits and pieces.
Additionally, there are some questions that your machine learning papers reviewer might ask you, so prepare their answers in advance.
- Did you get lucky with your choice of datasets?
- Why were the given parameters chosen for your experimental setup?
- Will your research findings also work on other datasets?
4. Avoid too much mathiness
Your research paper can have some formulas to describe your findings or concepts. But they should be put precisely so that the reader or the reviewer doesn’t take much time to understand them.
In many cases, when people overuse the formulae or provide spurious explanations to justify their finding, it reduces the impact of your research paper and you will lose a lot of readers as well, even if your paper gets published.
5. Abstract to be written at last
The abstract is one of the important aspects of a research paper is a vital part that is read by the majority of your readers. We advise you to write it at last so that you can include the key essences and takeaways of your research paper.
How to submit your machine learning research papers?
Once you complete your research paper, it is to be submitted under some policies set by the organizers of various journals. These policies are set up to ensure an established ecosystem that would encourage the machine learning practitioners who are writing research papers to volunteer for reproducing the claimed results.
In the new program introduced, there are three components that you should keep in mind.
- Code submission policy
- ML reproducibility checklist for claimed results
- Community-wide reproducibility challenge
They demanded these parameters from all machine learning papers in order to promote best practices and code repository assessments. It helps in eliminating the need to build your future work from scratch.
How are machine learning papers assessed?
Every year, the conferences and the journals receive thousands of research papers. There is an ML code completeness checklist that verifies the code repository in your research paper for artefacts and scripts provided in it.
In addition to the above, the further analysis of the paper by the reviewers sets the final decision on whether your paper will be published or not.
Do’s and don't of writing research paper
Every researcher wished to have their paper published in top journals. But, it isn’t that easy. There is a whole list of things that you should keep in mind while writing your research paper. We have elaborated on it below.
- Present your work precisely. Avoid writing stories. Justify your research with methodologies and innovative ideas that fellow researchers can follow.
- Maintain a certain flow of content in your research paper.
- Provide solid supportive arguments and pieces of evidence that justify your findings.
- Include scientific terminologies in your research paper.
- Refer to sources from diverse backgrounds for up-to-date and trustworthy information.
- Ensure that you proofread the paper several times to eliminate any possible errors.
- Avoid any kind of plagiarism in your research paper.
- Don’t just replicate Wikipedia. Instead, find trustworthy sources for your citation and create your own original piece of content.
- Don’t include incomplete information. Be honest with your readers and include all the aspects related to your work that would answer the queries in the reader's mind.
- Support each of your findings and don’t reveal any absurd reasons for doing the research.
- Avoid going beyond the recommended word limit to pose an impression of your seriousness about following the guidelines.
- Don’t include fillers in your research paper and stick to points that are sufficient.
With all said above, you will now know how to write research paper in machine learning. It will no longer be a challenge for you and will make things easier for you. We recommend you stick to the standards, as doing something new will increase the risk involved in getting your paper published. Just stick to the above-mentioned tips and tricks and you are good to go.
We hope you get your research paper published!
Author is a seasoned writer with a reputation for crafting highly engaging, well-researched, and useful content that is widely read by many of today's skilled programmers and developers.
Frequently Asked Questions
Yes, AI can write a research paper for you in less time than you would take to write it manually.
We have listed down some of the top journals where you can publish machine learning papers below.
- Elsevier Pattern Recognition
- Journal of Machine Learning Research
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Wiley International Journal of Intelligent Systems
- IEEE Transactions on Neural Networks and Learning Systems
Here is a list of some of the best research papers for machine learning.
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution By Paul Vicol, Luke Metz, and Jascha Sohl-Dickstein
Scalable nearest neighbor algorithms for high dimensional data By Lowe, D.G., & Muja, M.
Trends in extreme learning machines By Huang, G., Huang, G., Song, S., & You, K.
Solving high-dimensional parabolic PDEs using the tensor train format By Lorenz Richter, Leon Sallandt, and Nikolas Nüsken
Optimal complexity in decentralized training By researchers at Cornell University, Yucheng Lu and Christopher De Sa
Follow the procedure given below to write a dataset in your research paper.
Step 1: Navigate to your study folder and then “Manage” tab.
Step 2: Select “Manage datasets.”
Step 3: Select “Create new dataset.”
Check out some free platforms which will publish your machine learning papers for free.
- ScienceOpen
- Social Science Research Network
- Directory of Open Access Journals
- Education Resources Information Center
- arXiv e-Print Archive
An abstract is something that summarises your paper in a small paragraph. So, when you write it for your research paper, ensure that:
- Its word count is 300 or less.
- It includes the purpose of your paper.
- Your discovery or findings as an outcome of your research paper
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Best Research Papers in Recommender Systems
Table of Contents
An overview of some of the most prominent papers that use machine learning to make recommendations
Recommender systems have become an essential part of our online experience, providing personalized recommendations for products, services and content.
Over the years, researchers have developed and refined various approaches to improve recommendation algorithms, leading to a rich body of literature on the subject. In this article, we will review some of the best research papers in recommender systems.
Collaborative filtering for implicit feedback datasets
Collaborative Filtering for Implicit Feedback Datasets is a seminal paper published in 2008 by Yifan Hu, Yehuda Koren, and Chris Volinsky. The paper presented a collaborative filtering method using implicit feedback datasets. This approach is based on the factorization of the interaction matrix between users and items to discover latent factors that represent user and item preferences. The method was found by Mr perform well on large datasets such as files from e-commerce and social media platforms.
One of the key contributions of this paper is that it addresses the problem of sparsity in user interaction data. Traditional collaborative filtering methods suffer from sparsity, where most users only interact with a small fraction of available items, making it difficult to accurately predict user preferences. The method proposed in this paper can handle implicit feedback data such as clicks, impressions and purchases, which are abundant in online platforms. The paper showed that this method outperforms traditional collaborative filtering methods on several large datasets.
Neural collaborative filtering
Neural Collaborative Filtering is a paper published in 2017 by Xiangnan He, Lizi Liao, Hanwang Zhang, and Tat-Seng Chua. The paper presented a deep learning approach to collaborative filtering. The method uses neural networks to learn interactions between users, combining both explicit and implicit feedback to improve recommendation accuracy. The letter showed it Neural collaborative filtering has outperformed traditional collaborative filtering methods on several datasets.
The authors proposed a neural network architecture that combines the strengths of matrix factorization and multilayer perceptron models. The neural network takes both user and item features as input and learns a latent representation of users and items that can capture their preferences and characteristics. The paper also introduced a new training objective that optimizes the model for evaluation rather than prediction . The method has been shown to perform well on several large datasets, including the MovieLens and Yelp datasets.
Factorization machines
Factorization Machines is a paper published in 2010 by Steffen Rendle. The paper presented a new recommendation approach that combines the advantages of linear models and matrix factorization. The method uses factorization to capture user-item interactions, allowing the model to better generalize to new data. This approach has been shown to work well for a variety of recommendation tasks, including movie and music recommendations.
The article proposes a mathematical framework that enables efficient calculation of factorization models. This approach can handle high-dimensional and sparse data, making it suitable for large datasets. The method has been extended to handle more complex data structures, such as sequences and graphs, and has been applied to various recommendation tasks, including personalized advertising and social network recommendations.
Learning from implicit feedback
Learning from Implicit Feedback, published in 2002 by John Lafferty and Chi Wang, proposed a collaborative filtering method using implicit feedback data such as user clicks or purchases. This approach uses a Bayesian probabilistic framework to model user preferences and item characteristics, enabling personalized recommendations. The method was found to perform well on large datasets with implicit feedback.
Deep Neural Networks for YouTube Recommendations
Deep Neural Networks for YouTube Recommendations, published in 2016 by Paul Covington, Jay Adams, and Emre Sargin, introduced a deep learning-based recommendation approach specifically for YouTube. The method uses a hierarchical neural network architecture to model user behavior and recommend videos based on the user’s previous interactions. This approach was found to outperform traditional collaborative filtering methods in the YouTube recommendation task.
Recommender systems have become an essential tool for online platforms to provide users with personalized recommendations. The field of recommender systems has seen significant progress in recent years, with researchers developing new approaches to improve the accuracy and effectiveness of recommendations. In this article, we have reviewed some of the best research works in recommender systems, covering methods based on collaborative filtering, deep learning, and factorization. These approaches have been shown to work well for a variety of recommendation tasks and have the potential to further improve the online user experience.
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