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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

Latest research and reviews.

Research 01 March 2023 | Open Access

Evaluation of grouped capsule network for intracranial hemorrhage segmentation in CT scans

Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes

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.

Research 28 February 2023 | Open Access

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods

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.

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

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.

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.

Comments & Opinion | 24 February 2023

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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Join the community, trending research, the forward-forward algorithm: some preliminary investigations.

research papers machine learning

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.

research papers machine learning

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.

research papers machine learning

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.

research papers machine learning

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.

research papers machine learning

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.

research papers machine learning

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.

research papers machine learning

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.

research papers machine learning

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.

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research papers machine learning

Machine Learning

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Issue 2, February 2023

Latest articles

Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes.

research papers machine learning

Local2Global: a distributed approach for scaling representation learning on graphs

Authors (first, second and last of 5).

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Cross-model consensus of explanations and beyond for image classification models: an empirical study

research papers machine learning

Inverse learning in Hilbert scales

On the sample complexity of actor-critic method for reinforcement learning with function approximation

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Journal updates

Call for papers: special issue on discovery science 2022.

Submission Deadline: March 5, 2023

Guest Editor: Dino Ienco, Roberto Interdonato, Pascal Poncelet

Call for Papers: ECML PKDD 2023

Submission Deadlines: November 28, 2022 February 10, 2023

Special issue on “IFCS 2022 - Classification and Data Science in the Digital Age”

Paper submission (deadline for submissions): November 30, 2022

Guest Editors: Michaelangelo Ceci, João Gama, Jose Lozano, André de Carvalho, Paula Brito

Call for Papers: Special Issue on Safe and Fair Machine Learning

Guest editors: Dana Drachsler Cohen, Javier Garcia, Mohammad Ghavamzadeh, Marek Petrik, Philip S. Thomas Submission deadline: 28 February 2022

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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


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.

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Towards Data Science

Kurtis Pykes

Jun 30, 2021


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:

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.

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:

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?

What’s the key achievement?

What does the AI community think?

What are future research areas?

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.

Where can you get implementation code?

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.

What are possible business applications?

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%).

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.

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.

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 .

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).

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.

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.

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 .

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.

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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".

research papers machine learning

For each paper we also give the year it was published, a Highly Influential Citation count (HIC) and Citation Velocity (CV) measures provided by .  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 .

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.

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Using machine learning to predict high-impact research

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Illustration showing three sets of blue nodes (dots) surrounding a single orange node. In the first set, the orange node is labeled "Article of interest"

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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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research papers machine learning

Top Machine Learning Research Papers Released In 2020

research papers machine learning

It has been only two weeks into the last month of the year and, 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. 

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. 


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 ? @OriolVinyalsML : my take is: farewell convolutions : ) — 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

research papers machine learning

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

research papers machine learning

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. 


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

Outstanding Datasets and Benchmarks Papers

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.

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

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Getting Started with Research Papers on Machine Learning: What to Read & How

research papers machine learning

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

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.

Research papers on machine learning

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

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

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.

Research papers on machine learning

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?

Research papers on machine learning

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 .

Bonani Bose

A self-starter technical communicator, capable of working in an entrepreneurial environment producing all kinds of technical content including system manuals, product release notes, product user guides, tutorials, software installation guides, technical proposals, and white papers. Plus, an avid blogger and Social Media Marketing Enthusiast.

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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), …

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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.

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For developers, how to write a good research paper in the machine learning area.

Research paper on Machine Learning.

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

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.

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.

Types of research papers for machine learning.webp

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.

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.

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?

Method to write a good research paper.webp

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.

Additionally, there are some questions that your machine learning papers reviewer might ask you, so prepare their answers in advance.

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.

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.

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!

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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.

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.

An abstract is something that summarises your paper in a small paragraph. So, when you write it for your research paper, ensure that:

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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.

What’s great about this article? It was generated mostly using the prompts provided by ChatGPT!

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