- See Also
-
Links
- “Symbolic Discovery of Optimization Algorithms”, Et Al 2023
- “ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition”, Et Al 2022
- “Do Current Multi-Task Optimization Methods in Deep Learning Even Help?”, Et Al 2022
- “Selective Neutralization and Deterring of Cockroaches With Laser Automated by Machine Vision”, Et Al 2022
- “Git Re-Basin: Merging Models modulo Permutation Symmetries”, Et Al 2022
- “Learning With Differentiable Algorithms”, 2022
- “Normalized Activation Function: Toward Better Convergence”, 2022
- “Bugs in the Data: How ImageNet Misrepresents Biodiversity”, 2022
- “AniWho: A Quick and Accurate Way to Classify Anime Character Faces in Images”, Et Al 2022
- “The Value of Out-of-Distribution Data”, Et Al 2022
- “Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training”, Et Al 2022
- “Learning With Combinatorial Optimization Layers: a Probabilistic Approach”, Et Al 2022
- “What Do We Maximize in Self-Supervised Learning?”, Shwartz- Et Al 2022
- “Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit”, Et Al 2022
- “High-performing Neural Network Models of Visual Cortex Benefit from High Latent Dimensionality”, 2022
- “Predicting Word Learning in Children from the Performance of Computer Vision Systems”, Et Al 2022
- “Wav2Vec-Aug: Improved Self-supervised Training With Limited Data”, Et Al 2022
- “The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon”, Et Al 2022
- “An Improved One Millisecond Mobile Backbone”, Et Al 2022
- “Greedy Bayesian Posterior Approximation With Deep Ensembles”, 2022
- “Semantic Projection Recovers Rich Human Knowledge of Multiple Object Features from Word Embeddings”, Et Al 2022
- “Generating Scientific Claims for Zero-Shot Scientific Fact Checking”, Et Al 2022
- “Model Soups: Averaging Weights of Multiple Fine-tuned Models Improves Accuracy without Increasing Inference Time”, Et Al 2022
- “Deep Lexical Hypothesis: Identifying Personality Structure in Natural Language”, 2022
- “Gradients without Backpropagation”, Et Al 2022
- “Don’t Stop the Training: Continuously-updating Self-supervised Algorithms Best Account for Auditory Responses in the Cortex”, Et Al 2022
- “Towards Scaling Difference Target Propagation by Learning Backprop Targets”, Et Al 2022
- “M5 Accuracy Competition: Results, Findings, and Conclusions”, Et Al 2022
- “Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models”, Et Al 2022
- “Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow”, Et Al 2021
- “Artificial Intelligence ‘Sees’ Split Electrons”, 2021
- “Pushing the Frontiers of Density Functionals by Solving the Fractional Electron Problem”, Et Al 2021
- “ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction”, Et Al 2021
- “Word Golf”, 2021
- “Deep Learning Enables Genetic Analysis of the Human Thoracic Aorta”, Et Al 2021
- “Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks”, Et Al 2021
- “Achieving Human Parity on Visual Question Answering”, Et Al 2021
- “BC-Z: Zero-Shot Task Generalization With Robotic Imitation Learning”, Et Al 2021
- “Learning in High Dimension Always Amounts to Extrapolation”, Et Al 2021
- “TWIST: Self-Supervised Learning by Estimating Twin Class Distributions”, Et Al 2021
- “The Structure of Genotype-phenotype Maps Makes Fitness Landscapes Navigable”, Et Al 2021
- “The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks”, Et Al 2021
- “Deep Neural Networks and Tabular Data: A Survey”, Et Al 2021
- “Learning through Atypical”Phase Transitions” in Overparameterized Neural Networks”, Et Al 2021
- “RAFT: A Real-World Few-Shot Text Classification Benchmark”, Et Al 2021
- “PPT: Pre-trained Prompt Tuning for Few-shot Learning”, Et Al 2021
- “Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners”, Et Al 2021
- “ETA Prediction With Graph Neural Networks in Google Maps”, Derrow- Et Al 2021
- “Predictive Coding: a Theoretical and Experimental Review”, Et Al 2021
- “Neuroprosthesis for Decoding Speech in a Paralyzed Person With Anarthria”, Et Al 2021
- “A Connectivity-constrained Computational Account of Topographic Organization in Primate High-level Visual Cortex”, Et Al 2021
- “A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers”, Et Al 2021
- “Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation”, Et Al 2021
- “Revisiting Deep Learning Models for Tabular Data”, Et Al 2021
- “Randomness In Neural Network Training: Characterizing The Impact of Tooling”, Et Al 2021
- “BEiT: BERT Pre-Training of Image Transformers”, Et Al 2021
- “Revisiting Model Stitching to Compare Neural Representations”, Et Al 2021
- “Artificial Intelligence in China’s Revolution in Military Affairs”, 2021
- “The Geometry of Concept Learning”, Et Al 2021
- “VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning”, Et Al 2021
- “Understanding by Understanding Not: Modeling Negation in Language Models”, Et Al 2021
- “Entailment As Few-Shot Learner”, Et Al 2021
- “PAWS: Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments With Support Samples”, Et Al 2021
- “Computer Optimization: Your Computer Is Faster Than You Think”, 2021
- “Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus”, Santos- Et Al 2021
- “Rip Van Winkle’s Razor, a Simple New Estimate for Adaptive Data Analysis”, 2021
- “Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization”, Et Al 2021
- “Contrasting Contrastive Self-Supervised Representation Learning Models”, Et Al 2021
- “Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations”, Et Al 2021
- “GWAS in Almost 195,000 Individuals Identifies 50 Previously Unidentified Genetic Loci for Eye Color”, Et Al 2021
- “BERTese: Learning to Speak to BERT”, Et Al 2021
- “Predictive Coding Can Do Exact Backpropagation on Any Neural Network”, Et Al 2021
- “Barlow Twins: Self-Supervised Learning via Redundancy Reduction”, Et Al 2021
- “WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning”, Et Al 2021
- “Rip Van Winkle’s Razor: A Simple Estimate of Overfit to Test Data”, 2021
- “Image Completion via Inference in Deep Generative Models”, Et Al 2021
- “DirectPred: Understanding Self-supervised Learning Dynamics without Contrastive Pairs”, Et Al 2021
- “Explaining Neural Scaling Laws”, Et Al 2021
- “MLGO: a Machine Learning Guided Compiler Optimizations Framework”, Et Al 2021
- “Facial Recognition Technology Can Expose Political Orientation from Naturalistic Facial Images”, 2021
- “Solving Mixed Integer Programs Using Neural Networks”, Et Al 2020
- “Sixteen Facial Expressions Occur in Similar Contexts Worldwide”, 2020
- “PiRank: Learning To Rank via Differentiable Sorting”, Et Al 2020
- “Real-time Synthesis of Imagined Speech Processes from Minimally Invasive Recordings of Neural Activity”, Et Al 2020
- “Generalization Bounds for Deep Learning”, Valle-2020
- “Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games”, Et Al 2020
- “Inductive Biases for Deep Learning of Higher-Level Cognition”, 2020
- “Exploring Simple Siamese Representation Learning”, 2020
- “Recent Advances in Neurotechnologies With Broad Potential for Neuroscience Research”, Vázquez- Et Al 2020
- “Voting for Authorship Attribution Applied to Dark Web Data”, 2020
- “Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding”, Et Al 2020
- “Twenty Years Beyond the Turing Test: Moving Beyond the Human Judges Too”, Hernández-2020
- “Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary With Width and Depth”, Et Al 2020
- “Open-Domain Question Answering Goes Conversational via Question Rewriting”, Et Al 2020
- “Digital Voicing of Silent Speech”, 2020
- “Rank-Smoothed Pairwise Learning In Perceptual Quality Assessment”, Et Al 2020
- “Implicit Gradient Regularization”, 2020
- “It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners”, Schick & 2020
- “Large Associative Memory Problem in Neurobiology and Machine Learning”, 2020
- “Cultural Influences on Word Meanings Revealed through Large-scale Semantic Alignment”, Et Al 2020
- “AdapterHub: A Framework for Adapting Transformers”, Et Al 2020
- “On Linear Identifiability of Learned Representations”, Et Al 2020
- “Identifying Regulatory Elements via Deep Learning”, Et Al 2020
- “Is SGD a Bayesian Sampler? Well, Almost”, Et Al 2020
- “Bootstrap Your Own Latent (BYOL): A New Approach to Self-supervised Learning”, Et Al 2020
- “SCAN: Learning to Classify Images without Labels”, Et Al 2020
- “Open-Retrieval Conversational Question Answering”, Et Al 2020
- “Politeness Transfer: A Tag and Generate Approach”, Et Al 2020
- “Supervised Contrastive Learning”, Et Al 2020
- “Can You Put It All Together: Evaluating Conversational Agents’ Ability to Blend Skills”, Et Al 2020
- “Backpropagation and the Brain”, Et Al 2020
- “TREC CAsT 2019: The Conversational Assistance Track Overview”, Et Al 2020
- “Improved Baselines With Momentum Contrastive Learning”, Et Al 2020
- “The Large Learning Rate Phase of Deep Learning: the Catapult Mechanism”, Et Al 2020
- “Fast Differentiable Sorting and Ranking”, Et Al 2020
- “The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence”, 2020
- “Quantifying Independently Reproducible Machine Learning”, 2020
- “The Secret History of Facial Recognition: Sixty Years Ago, a Sharecropper’s Son Invented a Technology to Identify Faces. Then the Record of His Role All but Vanished. Who Was Woody Bledsoe, and Who Was He Working For?”, 2020
- “ImageNet-A: Natural Adversarial Examples”, Et Al 2020
- “Deep-Eyes: Fully Automatic Anime Character Colorization With Painting of Details on Empty Pupils”, Et Al 2020
- “Can the Brain Do Backpropagation? -Exact Implementation of Backpropagation in Predictive Coding Networks”, Et Al 2020
- “Identifying Statistical Bias in Dataset Replication [blog]”, Engstrom & Al 2020
- “Learning Neural Activations”, 2019
- “2019 AI Alignment Literature Review and Charity Comparison”, 2019
- “Libri-Light: A Benchmark for ASR With Limited or No Supervision”, Et Al 2019
- “Connecting Vision and Language With Localized Narratives”, Pont- Et Al 2019
- “12-in-1: Multi-Task Vision and Language Representation Learning”, Et Al 2019
- “2019 News”, 2019
- “On the Measure of Intelligence”, 2019
- “A Deep Learning Framework for Neuroscience”, Et Al 2019
- “Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules”, Sanchez- Et Al 2019
- “KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition With Deep Learning”, Et Al 2019
- “Approximate Inference in Discrete Distributions With Monte Carlo Tree Search and Value Functions”, Et Al 2019
- “Best Practices for the Human Evaluation of Automatically Generated Text”, Et Al 2019
- “RandAugment: Practical Automated Data Augmentation With a Reduced Search Space”, Et Al 2019
- “ALBERT: A Lite BERT for Self-supervised Learning of Language Representations”, Et Al 2019
- “Neural Networks Are a Priori Biased towards Boolean Functions With Low Entropy”, Et Al 2019
- “Engineering a Less Artificial Intelligence”, Et Al 2019
- “Emergent Tool Use From Multi-Agent Autocurricula”, Et Al 2019
- “A Step Toward Quantifying Independently Reproducible Machine Learning Research”, 2019
- “Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform”, Et Al 2019
- “Can One Concurrently Record Electrical Spikes from Every Neuron in a Mammalian Brain?”, Et Al 2019
- “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”, Et Al 2019
- “Deep Set Prediction Networks”, Et Al 2019
- “Optimizing Color for Camouflage and Visibility Using Deep Learning: the Effects of the Environment and the Observer’s Visual System”, Et Al 2019
- “Cold Case: The Lost MNIST Digits”, 2019
- “Speech2Face: Learning the Face Behind a Voice”, Et Al 2019
- “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”, Et Al 2019
- “Universal Quantum Control through Deep Reinforcement Learning”, Et Al 2019
- “Analysing Mathematical Reasoning Abilities of Neural Models”, Et Al 2019
- “Reinforcement Learning for Recommender Systems: A Case Study on Youtube”, 2019
- “Stochastic Optimization of Sorting Networks via Continuous Relaxations”, Et Al 2019
- “Surprises in High-Dimensional Ridgeless Least Squares Interpolation”, Et Al 2019
- “DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs”, Et Al 2019
- “Theories of Error Back-Propagation in the Brain”, 2019
- “A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images”, 2019
- “Unmasking Clever Hans Predictors and Assessing What Machines Really Learn”, Et Al 2019
- “What Makes a Good Conversation? How Controllable Attributes Affect Human Judgments”, Et Al 2019
- “The Evolved Transformer”, Et Al 2019
- “Forecasting Transformative AI: An Expert Survey”, Et Al 2019
- “Identifying Facial Phenotypes of Genetic Disorders Using Deep Learning”, Et Al 2019
- “High-performance Medicine: the Convergence of Human and Artificial Intelligence”, 2019
- “Why Is There No Successful Whole Brain Simulation (Yet)?”, 2019
- “Evaluation and Accurate Diagnoses of Pediatric Diseases Using Artificial Intelligence”, Et Al 2019
- “Reinventing the Wheel: Discovering the Optimal Rolling Shape With PyTorch”, 2019
- “An Empirical Study of Example Forgetting during Deep Neural Network Learning”, Et Al 2018
- “Evolution As Backstop for Reinforcement Learning”, 2018
- “CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge”, Et Al 2018
- “Depth With Nonlinearity Creates No Bad Local Minima in ResNets”, 2018
- “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, Et Al 2018
- “Interpretable Textual Neuron Representations for NLP”, Et Al 2018
- “Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes”, Et Al 2018
- “Searching for Efficient Multi-Scale Architectures for Dense Image Prediction”, Et Al 2018
- “Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction”, 2018
- “Searching Toward Pareto-Optimal Device-Aware Neural Architectures”, Et Al 2018
- “A Study of Reinforcement Learning for Neural Machine Translation”, Et Al 2018
- “Neural Arithmetic Logic Units”, Et Al 2018
- “Modeling Visual Context Is Key to Augmenting Object Detection Datasets”, Et Al 2018
- “Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search”, Et Al 2018
- “Automatically Composing Representation Transformations As a Means for Generalization”, Et Al 2018
- “ARPA and SCI: Surfing AI”, 2018
- “Differentiable Learning-to-Normalize via Switchable Normalization”, Et Al 2018
- “On the Spectral Bias of Neural Networks”, Et Al 2018
- “Neural Tangent Kernel: Convergence and Generalization in Neural Networks”, Et Al 2018
- “Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, Et Al 2018
- “Do CIFAR-10 Classifiers Generalize to CIFAR-10?”, Et Al 2018
- “Zero-Shot Dual Machine Translation”, Et Al 2018
- “Do Better ImageNet Models Transfer Better?”, Et Al 2018
- “GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding”, Et Al 2018
- “Adafactor: Adaptive Learning Rates With Sublinear Memory Cost”, 2018
- “Think You Have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge”, Et Al 2018
- “SentEval: An Evaluation Toolkit for Universal Sentence Representations”, 2018
- “Averaging Weights Leads to Wider Optima and Better Generalization”, Et Al 2018
- “Analyzing Uncertainty in Neural Machine Translation”, Et Al 2018
- “End-to-end Deep Image Reconstruction from Human Brain Activity”, Et Al 2018
- “Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari”, Et Al 2018
- “SignSGD: Compressed Optimisation for Non-Convex Problems”, Et Al 2018
- “Differentiable Dynamic Programming for Structured Prediction and Attention”, 2018
- “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction”, Et Al 2018
- “Semantic Projection: Recovering Human Knowledge of Multiple, Distinct Object Features from Word Embeddings”, Et Al 2018
- “Panoptic Segmentation”, Et Al 2018
- “Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning”, Et Al 2018
- “Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease”, Et Al 2018
- “Deep Image Reconstruction from Human Brain Activity”, Et Al 2017
- “China’s A.I. Advances Help Its Tech Industry, and State Security”, 2017
- “Three-dimensional Visualization and a Deep-learning Model Reveal Complex Fungal Parasite Networks in Behaviorally Manipulated Ants”, Et Al 2017
- “Decoupled Weight Decay Regularization”, 2017
- “Unsupervised Machine Translation Using Monolingual Corpora Only”, Et Al 2017
- “Automatic Differentiation in PyTorch”, Et Al 2017
- “Rethinking Generalization Requires Revisiting Old Ideas: Statistical Mechanics Approaches and Complex Learning Behavior”, 2017
- “Malware Detection by Eating a Whole EXE”, Et Al 2017
- “Mixup: Beyond Empirical Risk Minimization”, Et Al 2017
- “AlphaGo Zero: Mastering the Game of Go without Human Knowledge”, Et Al 2017
- “Swish: Searching for Activation Functions”, Et Al 2017
- “Online Learning of a Memory for Learning Rates”, Et Al 2017
- “Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates”, 2017
- “Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection”, Et Al 2017
- “Emergence of Locomotion Behaviours in Rich Environments”, Et Al 2017
- “Six Challenges for Neural Machine Translation”, 2017
- “Verb Physics: Relative Physical Knowledge of Actions and Objects”, 2017
- “Driver Identification Using Automobile Sensor Data from a Single Turn”, Et Al 2017
- “StreetStyle: Exploring World-wide Clothing Styles from Millions of Photos”, Et Al 2017
- “Deep Voice 2: Multi-Speaker Neural Text-to-Speech”, Et Al 2017
- “WebVision Challenge: Visual Learning and Understanding With Web Data”, Et Al 2017
- “Inferring and Executing Programs for Visual Reasoning”, Et Al 2017
- “Visual Attribute Transfer through Deep Image Analogy”, Et Al 2017
- “On Weight Initialization in Deep Neural Networks”, 2017
- “A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference”, Et Al 2017
- “RACE: Large-scale ReAding Comprehension Dataset From Examinations”, Et Al 2017
- “Data-efficient Deep Reinforcement Learning for Dexterous Manipulation”, Et Al 2017
- “Research Ideas”, 2017
- “Prototypical Networks for Few-shot Learning”, Et Al 2017
- “Meta Networks”, 2017
- “Understanding Synthetic Gradients and Decoupled Neural Interfaces”, Et Al 2017
- “Deep Voice: Real-time Neural Text-to-Speech”, Et Al 2017
- “Adaptive Neural Networks for Efficient Inference”, Et Al 2017
- “Machine Learning Predicts Laboratory Earthquakes”, Et Al 2017
- “Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks”, Et Al 2017
- “Dermatologist-level Classification of Skin Cancer With Deep Neural Networks”, Et Al 2017
- “Machine Learning for Systems and Systems for Machine Learning”, 2017
- “Feedback Networks”, Et Al 2016
- “CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning”, Et Al 2016
- “Towards Information-Seeking Agents”, Et Al 2016
- “Spatially Adaptive Computation Time for Residual Networks”, Et Al 2016
- “Deep Learning Reinvents the Hearing Aid: Finally, Wearers of Hearing Aids Can Pick out a Voice in a Crowded Room”, 2016
- “Self-critical Sequence Training for Image Captioning”, Et Al 2016
- “MS MARCO: A Human Generated MAchine Reading COmprehension Dataset”, Et Al 2016
- “Learning to Reinforcement Learn”, Et Al 2016
- “Lip Reading Sentences in the Wild”, Et Al 2016
- “Could a Neuroscientist Understand a Microprocessor?”, 2016
- “Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of ArXiv:1611.04135)”, 2016
- “A Neural Network Playground”, 2016
- “Deep Information Propagation”, Et Al 2016
- “Homotopy Analysis for Tensor PCA”, Et Al 2016
- “Language As a Latent Variable: Discrete Generative Models for Sentence Compression”, 2016
- “On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima”, Et Al 2016
- “Why Does Deep and Cheap Learning Work so Well?”, Et Al 2016
- “SGDR: Stochastic Gradient Descent With Warm Restarts”, 2016
- “Concrete Problems in AI Safety”, Et Al 2016
- “SQuAD: 100,000+ Questions for Machine Comprehension of Text”, Et Al 2016
- “Matching Networks for One Shot Learning”, Et Al 2016
- “Convolutional Sketch Inversion”, Et Al 2016
- “Unifying Count-Based Exploration and Intrinsic Motivation”, Et Al 2016
- “Synthesizing the Preferred Inputs for Neurons in Neural Networks via Deep Generator Networks”, Et Al 2016
- “Wide Residual Networks”, 2016
- “Improving Information Extraction by Acquiring External Evidence With Reinforcement Learning”, Et Al 2016
- “Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity”, Et Al 2016
- “ “Why Should I Trust You?”: Explaining the Predictions of Any Classifier”, Et Al 2016
- “Mastering the Game of Go With Deep Neural Networks and Tree Search”, Et Al 2016
- “Learning to Compose Neural Networks for Question Answering”, Et Al 2016
- “How a Japanese Cucumber Farmer Is Using Deep Learning and TensorFlow”, 2016
- “Random Gradient-Free Minimization of Convex Functions”, 2015
- “Data-dependent Initializations of Convolutional Neural Networks”, Et Al 2015
- “Online Batch Selection for Faster Training of Neural Networks”, 2015
- “Neural Module Networks”, Et Al 2015
- “Deep DPG (DDPG): Continuous Control With Deep Reinforcement Learning”, Et Al 2015
- “A Neural Algorithm of Artistic Style”, Et Al 2015
- “Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, 2015
- “VQA: Visual Question Answering”, Et Al 2015
- “Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks”, Et Al 2015
- “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”, Et Al 2015
- “Freeze-Thaw Bayesian Optimization”, Et Al 2014
- “Microsoft COCO: Common Objects in Context”, Et Al 2014
- “Deep Learning in Neural Networks: An Overview”, 2014
- “On Learning Where To Look”, 2014
- “Neural Networks, Manifolds, and Topology”, 2014
- “Exact Solutions to the Nonlinear Dynamics of Learning in Deep Linear Neural Networks”, Et Al 2013
- “Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science”, 2013
- “Surprisingly Turing-Complete”, 2012
- “Deep Gaussian Processes”, 2012
- “Timing Technology: Lessons From The Media Lab”, 2012
- “Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting”, Et Al 2012
- “The Neural Net Tank Urban Legend”, 2011
- “HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent”, Et Al 2011
- “How Complex Are Individual Differences?”, 2010
- “A Free Energy Principle for the Brain”, Et Al 2006
- “DARPA and the Quest for Machine Intelligence, 1983–1993”, 2002
- “Stigler’s Diet Problem Revisited”, 2001
- “Epistemic Autonomy through Adaptive Sensing”
- “Optimality in Biological and Artificial Networks?”, 1997
- “A Sociological Study of the Official History of the Perceptrons Controversy [1996]”, 1996
- “Statistical Mechanics of Generalization”, 1996
- “Learning and Generalization in a Two-layer Neural Network: The Role of the Vapnik-Chervonvenkis Dimension”, 1994
- “Evolving 3D Morphology and Behavior by Competition”, 1994
- “A Sociological Study of the Official History of the Perceptrons Controversy [1993]”, 1993
- “The Statistical Mechanics of Learning a Rule”, Et Al 1993
- “On Learning the Past Tenses of English Verbs”, Rumelhart & 1993
- “Statistical Mechanics of Learning from Examples”, Et Al 1992
- “Memorization Without Generalization in a Multilayered Neural Network”, Et Al 1992
- “Symbolic and Neural Learning Algorithms: An Experimental Comparison”, Et Al 1991
- “Exhaustive Learning”, Et Al 1990
- “Artificial Neural Networks, Back Propagation, and the Kelley-Bryson Gradient Procedure”, 1990
- “International Joint Conference on Neural Networks, January 15–19, 1990: Volume 2: Applications Track”, 1990
- “International Joint Conference on Neural Networks, January 15–19, 1990: Volume 1: Theory Track, Neural and Cognitive Sciences Track”, 1990
- “Parallel Distributed Processing: Implications for Cognition and Development”, 1989
- “The Brain As Template”, 1988
- “Learning Representations by Backpropagating Errors”, Et Al 1986b
- “Learning Internal Representations by Error Propagation”, Et Al 1986
- “Toward An Interactive Model Of Reading”, 1985
- “1971”
- “Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms”, 1962
- “A Proposal For The Dartmouth Summer Research Project On Artificial Intelligence”, McCarthy & Al 1955
- “Evolving Virtual Creatures”
- “Using Deep Learning to Create Professional-Level Photographs”
- “Automatic Photography With Google Clips”
- “Gsutil Config—Obtain Credentials and Create Configuration File”, 2023
- “Why Momentum Really Works”
- “Identifying Statistical Bias in Dataset Replication”
- “Reddit: Reinforcement Learning Subreddit”, 2023
- “Glow: Better Reversible Generative Models”
- “Deep Reinforcement Learning Doesn’t Work Yet”
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Symbolic Discovery of Optimization Algorithms”, Et Al 2023
“Symbolic Discovery of Optimization Algorithms”, 2023-02-13 (similar; bibliography)
“ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition”, Et Al 2022
“ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition”, 2022-10-24 (similar; bibliography)
“Do Current Multi-Task Optimization Methods in Deep Learning Even Help?”, Et Al 2022
“Do Current Multi-Task Optimization Methods in Deep Learning Even Help?”, 2022-09-23 (; similar)
“Selective Neutralization and Deterring of Cockroaches With Laser Automated by Machine Vision”, Et Al 2022
“Selective neutralization and deterring of cockroaches with laser automated by machine vision”, 2022-09-21 (; similar; bibliography)
“Git Re-Basin: Merging Models modulo Permutation Symmetries”, Et Al 2022
“Git Re-Basin: Merging Models modulo Permutation Symmetries”, 2022-09-11 (similar)
“Learning With Differentiable Algorithms”, 2022
“Learning with Differentiable Algorithms”, 2022-09-01 (; similar)
“Normalized Activation Function: Toward Better Convergence”, 2022
“Normalized Activation Function: Toward Better Convergence”, 2022-08-29 (similar)
“Bugs in the Data: How ImageNet Misrepresents Biodiversity”, 2022
“Bugs in the Data: How ImageNet Misrepresents Biodiversity”, 2022-08-24 (; similar)
“AniWho: A Quick and Accurate Way to Classify Anime Character Faces in Images”, Et Al 2022
“AniWho: A Quick and Accurate Way to Classify Anime Character Faces in Images”, 2022-08-23 (; similar; bibliography)
“The Value of Out-of-Distribution Data”, Et Al 2022
“The Value of Out-of-Distribution Data”, 2022-08-23 (similar)
“Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training”, Et Al 2022
“Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training”, 2022-08-12 (; similar)
“Learning With Combinatorial Optimization Layers: a Probabilistic Approach”, Et Al 2022
“Learning with Combinatorial Optimization Layers: a Probabilistic Approach”, 2022-07-27 (; similar)
“What Do We Maximize in Self-Supervised Learning?”, Shwartz- Et Al 2022
“What Do We Maximize in Self-Supervised Learning?”, 2022-07-20 (similar)
“Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit”, Et Al 2022
“Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit”, 2022-07-18 (; similar)
“High-performing Neural Network Models of Visual Cortex Benefit from High Latent Dimensionality”, 2022
“High-performing neural network models of visual cortex benefit from high latent dimensionality”, 2022-07-13 (; similar)
“Predicting Word Learning in Children from the Performance of Computer Vision Systems”, Et Al 2022
“Predicting Word Learning in Children from the Performance of Computer Vision Systems”, 2022-07-07 (; similar)
“Wav2Vec-Aug: Improved Self-supervised Training With Limited Data”, Et Al 2022
“Wav2Vec-Aug: Improved self-supervised training with limited data”, 2022-06-27 (similar)
“The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon”, Et Al 2022
“The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon”, 2022-06-10 (similar)
“An Improved One Millisecond Mobile Backbone”, Et Al 2022
“An Improved One millisecond Mobile Backbone”, 2022-06-08 (; similar)
“Greedy Bayesian Posterior Approximation With Deep Ensembles”, 2022
“Greedy Bayesian Posterior Approximation with Deep Ensembles”, 2022-06-03 (; similar)
“Semantic Projection Recovers Rich Human Knowledge of Multiple Object Features from Word Embeddings”, Et Al 2022
“Semantic projection recovers rich human knowledge of multiple object features from word embeddings”, 2022-04-14 (; similar; bibliography)
“Generating Scientific Claims for Zero-Shot Scientific Fact Checking”, Et Al 2022
“Generating Scientific Claims for Zero-Shot Scientific Fact Checking”, 2022-03-24 (; similar)
“Model Soups: Averaging Weights of Multiple Fine-tuned Models Improves Accuracy without Increasing Inference Time”, Et Al 2022
“Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time”, 2022-03-10 (backlinks; similar; bibliography)
“Deep Lexical Hypothesis: Identifying Personality Structure in Natural Language”, 2022
“Deep Lexical Hypothesis: Identifying personality structure in natural language”, 2022-03-03 (; similar)
“Gradients without Backpropagation”, Et Al 2022
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“Implicit Gradient Regularization”, 2020
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“It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners”, Schick & 2020
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“Large Associative Memory Problem in Neurobiology and Machine Learning”, 2020
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“On Linear Identifiability of Learned Representations”, Et Al 2020
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“On the Measure of Intelligence”, 2019
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“A Step Toward Quantifying Independently Reproducible Machine Learning Research”, 2019
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“An Empirical Study of Example Forgetting during Deep Neural Network Learning”, Et Al 2018
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“Evolution As Backstop for Reinforcement Learning”, 2018
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“CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge”, Et Al 2018
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“Depth With Nonlinearity Creates No Bad Local Minima in ResNets”, 2018
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“ARPA and SCI: Surfing AI”, 2018
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“Zero-Shot Dual Machine Translation”, Et Al 2018
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“Adafactor: Adaptive Learning Rates With Sublinear Memory Cost”, 2018
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“SentEval: An Evaluation Toolkit for Universal Sentence Representations”, 2018
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“SignSGD: Compressed Optimisation for Non-Convex Problems”, Et Al 2018
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“Differentiable Dynamic Programming for Structured Prediction and Attention”, 2018
“Differentiable Dynamic Programming for Structured Prediction and Attention”, 2018-02-11 (; backlinks; similar)
“UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction”, Et Al 2018
“UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction”, 2018-02-09 (backlinks; similar)
“Semantic Projection: Recovering Human Knowledge of Multiple, Distinct Object Features from Word Embeddings”, Et Al 2018
“Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings”, 2018-02-05 (backlinks; similar)
“Panoptic Segmentation”, Et Al 2018
“Panoptic Segmentation”, 2018-01-03 (similar)
“Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning”, Et Al 2018
“Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning”, 2018 (; backlinks; similar)
“Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease”, Et Al 2018
“Deep Image Reconstruction from Human Brain Activity”, Et Al 2017
“Deep image reconstruction from human brain activity”, 2017-12-30 (; similar)
“China’s A.I. Advances Help Its Tech Industry, and State Security”, 2017
“China’s A.I. Advances Help Its Tech Industry, and State Security”, 2017-12-03 (; backlinks; similar; bibliography)
“Three-dimensional Visualization and a Deep-learning Model Reveal Complex Fungal Parasite Networks in Behaviorally Manipulated Ants”, Et Al 2017
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“Decoupled Weight Decay Regularization”, 2017
“Decoupled Weight Decay Regularization”, 2017-11-14 (backlinks; similar)
“Unsupervised Machine Translation Using Monolingual Corpora Only”, Et Al 2017
“Unsupervised Machine Translation Using Monolingual Corpora Only”, 2017-10-31 (backlinks; similar)
“Automatic Differentiation in PyTorch”, Et Al 2017
“Automatic differentiation in PyTorch”, 2017-10-28 (; backlinks; similar)
“Rethinking Generalization Requires Revisiting Old Ideas: Statistical Mechanics Approaches and Complex Learning Behavior”, 2017
“Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior”, 2017-10-26 (; similar)
“Malware Detection by Eating a Whole EXE”, Et Al 2017
“Malware Detection by Eating a Whole EXE”, 2017-10-25 (; backlinks; similar)
“Mixup: Beyond Empirical Risk Minimization”, Et Al 2017
“mixup: Beyond Empirical Risk Minimization”, 2017-10-25 (backlinks; similar)
“AlphaGo Zero: Mastering the Game of Go without Human Knowledge”, Et Al 2017
“AlphaGo Zero: Mastering the game of Go without human knowledge”, 2017-10-19 (; similar; bibliography)
“Swish: Searching for Activation Functions”, Et Al 2017
“Swish: Searching for Activation Functions”, 2017-10-16 (; similar)
“Online Learning of a Memory for Learning Rates”, Et Al 2017
“Online Learning of a Memory for Learning Rates”, 2017-09-20 (; backlinks; similar)
“Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates”, 2017
“Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates”, 2017-08-23 (backlinks; similar; bibliography)
“Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection”, Et Al 2017
“Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection”, 2017-08-04 (backlinks; similar)
“Emergence of Locomotion Behaviours in Rich Environments”, Et Al 2017
“Emergence of Locomotion Behaviours in Rich Environments”, 2017-07-07 (; similar)
“Six Challenges for Neural Machine Translation”, 2017
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“Verb Physics: Relative Physical Knowledge of Actions and Objects”, 2017
“Verb Physics: Relative Physical Knowledge of Actions and Objects”, 2017-06-12 (backlinks; similar)
“Driver Identification Using Automobile Sensor Data from a Single Turn”, Et Al 2017
“Driver Identification Using Automobile Sensor Data from a Single Turn”, 2017-06-09 (; backlinks; similar)
“StreetStyle: Exploring World-wide Clothing Styles from Millions of Photos”, Et Al 2017
“StreetStyle: Exploring world-wide clothing styles from millions of photos”, 2017-06-06 (; backlinks; similar)
“Deep Voice 2: Multi-Speaker Neural Text-to-Speech”, Et Al 2017
“Deep Voice 2: Multi-Speaker Neural Text-to-Speech”, 2017-05-24 (similar)
“WebVision Challenge: Visual Learning and Understanding With Web Data”, Et Al 2017
“WebVision Challenge: Visual Learning and Understanding With Web Data”, 2017-05-16 (; backlinks; similar; bibliography)
“Inferring and Executing Programs for Visual Reasoning”, Et Al 2017
“Inferring and Executing Programs for Visual Reasoning”, 2017-05-10 (; backlinks; similar)
“Visual Attribute Transfer through Deep Image Analogy”, Et Al 2017
“Visual Attribute Transfer through Deep Image Analogy”, 2017-05-02 (backlinks; similar)
“On Weight Initialization in Deep Neural Networks”, 2017
“On weight initialization in deep neural networks”, 2017-04-28 (backlinks; similar)
“A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference”, Et Al 2017
“A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference”, 2017-04-18 (backlinks; similar)
“RACE: Large-scale ReAding Comprehension Dataset From Examinations”, Et Al 2017
“RACE: Large-scale ReAding Comprehension Dataset From Examinations”, 2017-04-15 (; backlinks; similar)
“Data-efficient Deep Reinforcement Learning for Dexterous Manipulation”, Et Al 2017
“Data-efficient Deep Reinforcement Learning for Dexterous Manipulation”, 2017-04-10 (; similar)
“Research Ideas”, 2017
“Research Ideas”, 2017-03-19 (backlinks; similar; bibliography)
“Prototypical Networks for Few-shot Learning”, Et Al 2017
“Prototypical Networks for Few-shot Learning”, 2017-03-15 (; backlinks; similar)
“Meta Networks”, 2017
“Meta Networks”, 2017-03-02 (; backlinks; similar)
“Understanding Synthetic Gradients and Decoupled Neural Interfaces”, Et Al 2017
“Understanding Synthetic Gradients and Decoupled Neural Interfaces”, 2017-03-01 (; similar)
“Deep Voice: Real-time Neural Text-to-Speech”, Et Al 2017
“Deep Voice: Real-time Neural Text-to-Speech”, 2017-02-25 (similar)
“Adaptive Neural Networks for Efficient Inference”, Et Al 2017
“Adaptive Neural Networks for Efficient Inference”, 2017-02-25 (backlinks; similar)
“Machine Learning Predicts Laboratory Earthquakes”, Et Al 2017
“Machine Learning Predicts Laboratory Earthquakes”, 2017-02-19 (similar)
“Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks”, Et Al 2017
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“Dermatologist-level Classification of Skin Cancer With Deep Neural Networks”, Et Al 2017
“Machine Learning for Systems and Systems for Machine Learning”, 2017
“Machine Learning for Systems and Systems for Machine Learning”, 2017 (; backlinks)
“Feedback Networks”, Et Al 2016
“Feedback Networks”, 2016-12-30 (backlinks; similar)
“CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning”, Et Al 2016
“CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning”, 2016-12-20 (backlinks; similar)
“Towards Information-Seeking Agents”, Et Al 2016
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“Spatially Adaptive Computation Time for Residual Networks”, Et Al 2016
“Spatially Adaptive Computation Time for Residual Networks”, 2016-12-07 (backlinks; similar; bibliography)
“Deep Learning Reinvents the Hearing Aid: Finally, Wearers of Hearing Aids Can Pick out a Voice in a Crowded Room”, 2016
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“Self-critical Sequence Training for Image Captioning”, Et Al 2016
“Self-critical Sequence Training for Image Captioning”, 2016-12-02 (; backlinks; similar; bibliography)
“MS MARCO: A Human Generated MAchine Reading COmprehension Dataset”, Et Al 2016
“MS MARCO: A Human Generated MAchine Reading COmprehension Dataset”, 2016-11-28 (similar)
“Learning to Reinforcement Learn”, Et Al 2016
“Learning to reinforcement learn”, 2016-11-17 (; similar)
“Lip Reading Sentences in the Wild”, Et Al 2016
“Lip Reading Sentences in the Wild”, 2016-11-16 (similar)
“Could a Neuroscientist Understand a Microprocessor?”, 2016
“Could a Neuroscientist Understand a Microprocessor?”, 2016-11-14 (; backlinks; similar)
“Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of ArXiv:1611.04135)”, 2016
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“A Neural Network Playground”, 2016
“A Neural Network Playground”, 2016-11-05 (; similar)
“Deep Information Propagation”, Et Al 2016
“Deep Information Propagation”, 2016-11-04 (backlinks; similar)
“Homotopy Analysis for Tensor PCA”, Et Al 2016
“Homotopy Analysis for Tensor PCA”, 2016-10-28 (backlinks; similar)
“Language As a Latent Variable: Discrete Generative Models for Sentence Compression”, 2016
“Language as a Latent Variable: Discrete Generative Models for Sentence Compression”, 2016-09-23 (backlinks; similar)
“On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima”, Et Al 2016
“On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima”, 2016-09-15 (; similar)
“Why Does Deep and Cheap Learning Work so Well?”, Et Al 2016
“Why does deep and cheap learning work so well?”, 2016-08-29 (similar)
“SGDR: Stochastic Gradient Descent With Warm Restarts”, 2016
“SGDR: Stochastic Gradient Descent with Warm Restarts”, 2016-08-13 (backlinks; similar)
“Concrete Problems in AI Safety”, Et Al 2016
“Concrete Problems in AI Safety”, 2016-06-21 (; backlinks; similar)
“SQuAD: 100,000+ Questions for Machine Comprehension of Text”, Et Al 2016
“SQuAD: 100,000+ Questions for Machine Comprehension of Text”, 2016-06-16 (backlinks; similar)
“Matching Networks for One Shot Learning”, Et Al 2016
“Matching Networks for One Shot Learning”, 2016-06-13 (; similar)
“Convolutional Sketch Inversion”, Et Al 2016
“Convolutional Sketch Inversion”, 2016-06-09 (; similar)
“Unifying Count-Based Exploration and Intrinsic Motivation”, Et Al 2016
“Unifying Count-Based Exploration and Intrinsic Motivation”, 2016-06-06 (; similar)
“Synthesizing the Preferred Inputs for Neurons in Neural Networks via Deep Generator Networks”, Et Al 2016
“Synthesizing the preferred inputs for neurons in neural networks via deep generator networks”, 2016-05-30 (backlinks; similar)
“Wide Residual Networks”, 2016
“Wide Residual Networks”, 2016-05-23 (backlinks; similar; bibliography)
“Improving Information Extraction by Acquiring External Evidence With Reinforcement Learning”, Et Al 2016
“Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning”, 2016-03-25 (; backlinks; similar)
“Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity”, Et Al 2016
“Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity”, 2016-02-18 (backlinks; similar)
“ “Why Should I Trust You?”: Explaining the Predictions of Any Classifier”, Et Al 2016
“"Why Should I Trust You?": Explaining the Predictions of Any Classifier”, 2016-02-16 (; backlinks; similar)
“Mastering the Game of Go With Deep Neural Networks and Tree Search”, Et Al 2016
“Mastering the game of Go with deep neural networks and tree search”, 2016-01-28 (; similar)
“Learning to Compose Neural Networks for Question Answering”, Et Al 2016
“Learning to Compose Neural Networks for Question Answering”, 2016-01-07 (backlinks; similar)
“How a Japanese Cucumber Farmer Is Using Deep Learning and TensorFlow”, 2016
“Random Gradient-Free Minimization of Convex Functions”, 2015
“Random Gradient-Free Minimization of Convex Functions”, 2015-11-30 (; backlinks; similar)
“Data-dependent Initializations of Convolutional Neural Networks”, Et Al 2015
“Data-dependent Initializations of Convolutional Neural Networks”, 2015-11-21 (backlinks; similar)
“Online Batch Selection for Faster Training of Neural Networks”, 2015
“Online Batch Selection for Faster Training of Neural Networks”, 2015-11-19 (; backlinks; similar)
“Neural Module Networks”, Et Al 2015
“Neural Module Networks”, 2015-11-09 (; backlinks; similar)
“Deep DPG (DDPG): Continuous Control With Deep Reinforcement Learning”, Et Al 2015
“Deep DPG (DDPG): Continuous control with deep reinforcement learning”, 2015-09-09 (; similar)
“A Neural Algorithm of Artistic Style”, Et Al 2015
“A Neural Algorithm of Artistic Style”, 2015-08-26 (; backlinks; similar)
“Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, 2015
“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, 2015-06-06 (; similar)
“VQA: Visual Question Answering”, Et Al 2015
“VQA: Visual Question Answering”, 2015-05-03 (backlinks; similar)
“Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks”, Et Al 2015
“Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks”, 2015-02-19 (backlinks; similar)
“Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”, Et Al 2015
“Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification”, 2015-02-06 (similar)
“Freeze-Thaw Bayesian Optimization”, Et Al 2014
“Freeze-Thaw Bayesian Optimization”, 2014-06-16 (; backlinks; similar)
“Microsoft COCO: Common Objects in Context”, Et Al 2014
“Microsoft COCO: Common Objects in Context”, 2014-05-01 (; similar)
“Deep Learning in Neural Networks: An Overview”, 2014
“Deep Learning in Neural Networks: An Overview”, 2014-04-30 (; similar)
“On Learning Where To Look”, 2014
“On Learning Where To Look”, 2014-04-24 (backlinks; similar)
“Neural Networks, Manifolds, and Topology”, 2014
“Neural Networks, Manifolds, and Topology”, 2014-04-06 (; backlinks; similar)
“Exact Solutions to the Nonlinear Dynamics of Learning in Deep Linear Neural Networks”, Et Al 2013
“Exact solutions to the nonlinear dynamics of learning in deep linear neural networks”, 2013-12-20 (; backlinks; similar)
“Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science”, 2013
“Whatever next? Predictive brains, situated agents, and the future of cognitive science”, 2013-06-01 (; backlinks; similar)
“Surprisingly Turing-Complete”, 2012
“Surprisingly Turing-Complete”, 2012-12-09 (; backlinks; similar; bibliography)
“Deep Gaussian Processes”, 2012
“Deep Gaussian Processes”, 2012-11-02 (; backlinks; similar)
“Timing Technology: Lessons From The Media Lab”, 2012
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“Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting”, Et Al 2012
“Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting”, 2012-06-18 (; backlinks; similar)
“The Neural Net Tank Urban Legend”, 2011
“The Neural Net Tank Urban Legend”, 2011-09-20 (; backlinks; similar; bibliography)
“HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent”, Et Al 2011
“HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent”, 2011-06-28 (; backlinks; similar)
“How Complex Are Individual Differences?”, 2010
“How Complex Are Individual Differences?”, 2010-06-23 (; backlinks; similar; bibliography)
“A Free Energy Principle for the Brain”, Et Al 2006
“A free energy principle for the brain”, 2006-07-01 (; backlinks; similar)
“DARPA and the Quest for Machine Intelligence, 1983–1993”, 2002
“DARPA and the Quest for Machine Intelligence, 1983–1993”, 2002 (backlinks; similar)
“Stigler’s Diet Problem Revisited”, 2001
“Stigler’s Diet Problem Revisited”, 2001 (; backlinks)
“Epistemic Autonomy through Adaptive Sensing”
“Optimality in Biological and Artificial Networks?”, 1997
“A Sociological Study of the Official History of the Perceptrons Controversy [1996]”, 1996
“A Sociological Study of the Official History of the Perceptrons Controversy [1996]”, 1996-08-01 (backlinks; similar)
“Statistical Mechanics of Generalization”, 1996
“Learning and Generalization in a Two-layer Neural Network: The Role of the Vapnik-Chervonvenkis Dimension”, 1994
“Learning and generalization in a two-layer neural network: The role of the Vapnik-Chervonvenkis dimension”, 1994-03-28 (similar; bibliography)
“Evolving 3D Morphology and Behavior by Competition”, 1994
“A Sociological Study of the Official History of the Perceptrons Controversy [1993]”, 1993
“A Sociological Study of the Official History of the Perceptrons Controversy [1993]”, 1993-08 (; backlinks; similar; bibliography)
“The Statistical Mechanics of Learning a Rule”, Et Al 1993
“The statistical mechanics of learning a rule”, 1993-04-01 (backlinks)
“On Learning the Past Tenses of English Verbs”, Rumelhart & 1993
“On Learning the Past Tenses of English Verbs”, 1993 (similar)
“Statistical Mechanics of Learning from Examples”, Et Al 1992
“Statistical mechanics of learning from examples”, 1992-04 (backlinks; similar; bibliography)
“Memorization Without Generalization in a Multilayered Neural Network”, Et Al 1992
“Memorization Without Generalization in a Multilayered Neural Network”, 1992-01 (similar; bibliography)
“Symbolic and Neural Learning Algorithms: An Experimental Comparison”, Et Al 1991
“Symbolic and neural learning algorithms: An experimental comparison”, 1991-03-01 (; backlinks; similar)
“Exhaustive Learning”, Et Al 1990
“Exhaustive Learning”, 1990-09-01 (; similar)
“Artificial Neural Networks, Back Propagation, and the Kelley-Bryson Gradient Procedure”, 1990
“International Joint Conference on Neural Networks, January 15–19, 1990: Volume 2: Applications Track”, 1990
“International Joint Conference on Neural Networks, January 15–19, 1990: Volume 1: Theory Track, Neural and Cognitive Sciences Track”, 1990
“Parallel Distributed Processing: Implications for Cognition and Development”, 1989
“Parallel Distributed Processing: Implications for Cognition and Development”, 1989-01 (; backlinks; similar; bibliography)
“The Brain As Template”, 1988
“The Brain as Template”, 1988-06-01 ()
“Learning Representations by Backpropagating Errors”, Et Al 1986b
“Learning representations by backpropagating errors”, 1986-10-09 (backlinks; similar)
“Learning Internal Representations by Error Propagation”, Et Al 1986
“Learning Internal Representations by Error Propagation”, 1986 (backlinks; similar)
“Toward An Interactive Model Of Reading”, 1985
“1971”
“Metzger 1971”1980 (; backlinks)
“Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms”, 1962
“A Proposal For The Dartmouth Summer Research Project On Artificial Intelligence”, McCarthy & Al 1955
“Evolving Virtual Creatures”
“Using Deep Learning to Create Professional-Level Photographs”
“Automatic Photography With Google Clips”
“Gsutil Config—Obtain Credentials and Create Configuration File”, 2023
“gsutil config—Obtain credentials and create configuration file”, (; backlinks)
“Why Momentum Really Works”
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“Reddit: Reinforcement Learning Subreddit”, 2023
“Glow: Better Reversible Generative Models”
“Deep Reinforcement Learning Doesn’t Work Yet”
Wikipedia
Miscellaneous
Link Bibliography
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https://arxiv.org/abs/2302.06675#google: “Symbolic Discovery of Optimization Algorithms”, : -
https://arxiv.org/abs/2210.13352#huggingface: “ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition”, Sanchit Gandhi, Patrick von Platen, Alexander M. Rush: -
https://www.tandfonline.com/doi/full/10.1080/00305316.2022.2121777: “Selective Neutralization and Deterring of Cockroaches With Laser Automated by Machine Vision”, Ildar Rakhmatulin, Mathieu Lihoreau, Jose Pueyo: -
https://arxiv.org/abs/2208.11012: “AniWho: A Quick and Accurate Way to Classify Anime Character Faces in Images”, Martinus Grady Naftali, Jason Sebastian Sulistyawan, Kelvin Julian, Felix Indra Kurniadi: -
2022-grand.pdf: “Semantic Projection Recovers Rich Human Knowledge of Multiple Object Features from Word Embeddings”, Gabriel Grand, Idan Asher Blank, Francisco Pereira, Evelina Fedorenko: -
https://arxiv.org/abs/2203.05482: “Model Soups: Averaging Weights of Multiple Fine-tuned Models Improves Accuracy without Increasing Inference Time”, : -
https://arxiv.org/abs/2201.13415: “Towards Scaling Difference Target Propagation by Learning Backprop Targets”, : -
https://www.sciencedirect.com/science/article/pii/S0169207021001874: “M5 Accuracy Competition: Results, Findings, and Conclusions”, Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos: -
https://arxiv.org/abs/2112.13314: “Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow”, Florian Tambon, Amin Nikanjam, Le An, Foutse Khomh, Giuliano Antoniol: -
2021-kirkpatrick.pdf#deepmind: “Pushing the Frontiers of Density Functionals by Solving the Fractional Electron Problem”, : -
https://www.word.golf/: “Word Golf”, Eric Xia: -
https://arxiv.org/abs/2110.07402#bytedance: “TWIST: Self-Supervised Learning by Estimating Twin Class Distributions”, Feng Wang, Tao Kong, Rufeng Zhang, Huaping Liu, Hang Li: -
2021-moses.pdf: “Neuroprosthesis for Decoding Speech in a Paralyzed Person With Anarthria”, : -
https://arxiv.org/abs/2106.08254#microsoft: “BEiT: BERT Pre-Training of Image Transformers”, Hangbo Bao, Li Dong, Furu Wei: -
https://arxiv.org/abs/2104.13963#facebook: “PAWS: Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments With Support Samples”, Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Arm, Joulin, Nicolas Ballas, Michael Rabbat: -
http://www.offconvex.org/2021/04/07/ripvanwinkle/: “Rip Van Winkle’s Razor, a Simple New Estimate for Adaptive Data Analysis”, Sanjeev Arora, Yi Zhang: -
https://arxiv.org/abs/2103.14005: “Contrasting Contrastive Self-Supervised Representation Learning Models”, Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi: -
https://arxiv.org/abs/2103.12719#facebook: “Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations”, Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos: -
https://arxiv.org/abs/2102.06810#facebook: “DirectPred: Understanding Self-supervised Learning Dynamics without Contrastive Pairs”, Yuandong Tian, Xinlei Chen, Surya Ganguli: -
https://arxiv.org/abs/2007.07779: “AdapterHub: A Framework for Adapting Transformers”, : -
https://arxiv.org/abs/2005.12320: “SCAN: Learning to Classify Images without Labels”, Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Luc Van Gool: -
https://arxiv.org/abs/2004.11362#google: “Supervised Contrastive Learning”, : -
https://www.lesswrong.com/posts/SmDziGM9hBjW9DKmf/2019-ai-alignment-literature-review-and-charity-comparison: “2019 AI Alignment Literature Review and Charity Comparison”, Larks: -
https://arxiv.org/abs/1912.03098#google: “Connecting Vision and Language With Localized Narratives”, Jordi Pont-Tuset, Jasper Uijlings, Soravit Changpinyo, Radu Soricut, Vittorio Ferrari: -
13: “2019 News”, Gwern Branwen: -
https://arxiv.org/abs/1909.11942#google: “ALBERT: A Lite BERT for Self-supervised Learning of Language Representations”, Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut: -
https://arxiv.org/abs/1905.00537: “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”, : -
backstop: “Evolution As Backstop for Reinforcement Learning”, Gwern Branwen: -
arpa: “ARPA and SCI: Surfing AI”, Gwern Branwen: -
https://arxiv.org/abs/1806.10779: “Differentiable Learning-to-Normalize via Switchable Normalization”, Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, Jingyu Li: -
https://arxiv.org/abs/1803.05407: “Averaging Weights Leads to Wider Optima and Better Generalization”, Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson: -
https://arxiv.org/abs/1802.08842: “Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari”, Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter: -
https://www.nytimes.com/2017/12/03/business/china-artificial-intelligence.html: “China’s A.I. Advances Help Its Tech Industry, and State Security”, Paul Mozur, Keith Bradsher: -
2017-silver.pdf#deepmind: “AlphaGo Zero: Mastering the Game of Go without Human Knowledge”, : -
https://arxiv.org/abs/1708.07120: “Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates”, Leslie N. Smith, Nicholay Topin: -
https://arxiv.org/abs/1705.05640: “WebVision Challenge: Visual Learning and Understanding With Web Data”, Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, Jesse Berent, Abhinav Gupta, Rahul Sukthankar, Luc Van Gool: -
idea: “Research Ideas”, Gwern Branwen: -
https://arxiv.org/abs/1612.02297: “Spatially Adaptive Computation Time for Residual Networks”, Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov: -
https://arxiv.org/abs/1612.00563: “Self-critical Sequence Training for Image Captioning”, Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, Vaibhava Goel: -
https://arxiv.org/abs/1605.07146: “Wide Residual Networks”, Sergey Zagoruyko, Nikos Komodakis: -
turing-complete: “Surprisingly Turing-Complete”, Gwern Branwen: -
timing: “Timing Technology: Lessons From The Media Lab”, Gwern Branwen: -
tank: “The Neural Net Tank Urban Legend”, Gwern Branwen: -
difference: “How Complex Are Individual Differences?”, Gwern Branwen: -
1994-opper.pdf: “Learning and Generalization in a Two-layer Neural Network: The Role of the Vapnik-Chervonvenkis Dimension”, Manfred Opper: -
1993-olazaran.pdf: “A Sociological Study of the Official History of the Perceptrons Controversy [1993]”, Mikel Olazaran: -
1992-seung.pdf: “Statistical Mechanics of Learning from Examples”, H. S. Seung, H. Sompolinsky, N. Tishby: -
1992-hansel.pdf: “Memorization Without Generalization in a Multilayered Neural Network”, D. Hansel, G. Mato, C. Meunier: -
1989-mcclelland.pdf: “Parallel Distributed Processing: Implications for Cognition and Development”, James L. McClelland: