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See Also

Links

“RWKV: Reinventing RNNs for the Transformer Era”, Peng Et Al 2023

“RWKV: Reinventing RNNs for the Transformer Era”

“Resurrecting Recurrent Neural Networks for Long Sequences”, Orvieto Et Al 2023

“Resurrecting Recurrent Neural Networks for Long Sequences”

“SpikeGPT: Generative Pre-trained Language Model With Spiking Neural Networks”, Zhu Et Al 2023

“SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks”

“Organic Reaction Mechanism Classification Using Machine Learning”, Burés & Larrosa2023

“Organic reaction mechanism classification using machine learning”

“A High-performance Speech Neuroprosthesis”, Willett Et Al 2023

“A high-performance speech neuroprosthesis”

“Hungry Hungry Hippos: Towards Language Modeling With State Space Models”, Fu Et Al 2022

“Hungry Hungry Hippos: Towards Language Modeling with State Space Models”

“Melting Pot 2.0”, Agapiou Et Al 2022

“Melting Pot 2.0”

“VeLO: Training Versatile Learned Optimizers by Scaling Up”, Metz Et Al 2022

“VeLO: Training Versatile Learned Optimizers by Scaling Up”

“Legged Locomotion in Challenging Terrains Using Egocentric Vision”, Agarwal Et Al 2022

“Legged Locomotion in Challenging Terrains using Egocentric Vision”

“Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities”, Tjandra Et Al 2022

“Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities”

“Semantic Scene Descriptions As an Objective of Human Vision”, Doerig Et Al 2022

“Semantic scene descriptions as an objective of human vision”

“Benchmarking Compositionality With Formal Languages”, Valvoda Et Al 2022

“Benchmarking Compositionality with Formal Languages”

“PI-ARS: Accelerating Evolution-Learned Visual-Locomotion With Predictive Information Representations”, Lee Et Al 2022

“PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations”

“Spatial Representation by Ramping Activity of Neurons in the Retrohippocampal Cortex”, Tennant Et Al 2022

“Spatial representation by ramping activity of neurons in the retrohippocampal cortex”

“Neural Networks and the Chomsky Hierarchy”, Delétang Et Al 2022

“Neural Networks and the Chomsky Hierarchy”

“BYOL-Explore: Exploration by Bootstrapped Prediction”, Guo Et Al 2022

“BYOL-Explore: Exploration by Bootstrapped Prediction”

“AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos”, Wu Et Al 2022

“AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos”

“Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline (3RL)”, Caccia Et Al 2022

“Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline (3RL)”

“Simple Recurrence Improves Masked Language Models”, Lei Et Al 2022

“Simple Recurrence Improves Masked Language Models”

“Sequencer: Deep LSTM for Image Classification”, Tatsunami & Taki2022

“Sequencer: Deep LSTM for Image Classification”

“Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”, Chan Et Al 2022

“Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”

“Block-Recurrent Transformers”, Hutchins Et Al 2022

“Block-Recurrent Transformers”

“Learning by Directional Gradient Descent”, Silver Et Al 2022

“Learning by Directional Gradient Descent”

“Retrieval-Augmented Reinforcement Learning”, Goyal Et Al 2022

“Retrieval-Augmented Reinforcement Learning”

“General-purpose, Long-context Autoregressive Modeling With Perceiver AR”, Hawthorne Et Al 2022

“General-purpose, long-context autoregressive modeling with Perceiver AR”

“End-to-end Algorithm Synthesis With Recurrent Networks: Logical Extrapolation Without Overthinking”, Bansal Et Al 2022

“End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking”

“Data Scaling Laws in NMT: The Effect of Noise and Architecture”, Bansal Et Al 2022

“Data Scaling Laws in NMT: The Effect of Noise and Architecture”

“Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies”, Gklezakos & Rao2022

“Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies”

“Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild”, Miki Et Al 2022

“Learning robust perceptive locomotion for quadrupedal robots in the wild”

“Inducing Causal Structure for Interpretable Neural Networks (IIT)”, Geiger Et Al 2021

“Inducing Causal Structure for Interpretable Neural Networks (IIT)”

“Evaluating Distributional Distortion in Neural Language Modeling”, Anonymous2021

“Evaluating Distributional Distortion in Neural Language Modeling”

“Gradients Are Not All You Need”, Metz Et Al 2021

“Gradients are Not All You Need”

“An Explanation of In-context Learning As Implicit Bayesian Inference”, Xie Et Al 2021

“An Explanation of In-context Learning as Implicit Bayesian Inference”

“Minimum Description Length Recurrent Neural Networks”, Lan Et Al 2021

“Minimum Description Length Recurrent Neural Networks”

“S4: Efficiently Modeling Long Sequences With Structured State Spaces”, Gu Et Al 2021

“S4: Efficiently Modeling Long Sequences with Structured State Spaces”

“A Connectome of The Drosophila Central Complex Reveals Network Motifs Suitable for Flexible Navigation and Context-dependent Action Selection”, Hulse Et Al 2021

“A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection”

“LSSL: Combining Recurrent, Convolutional, and Continuous-time Models With Linear State-Space Layers”, Gu Et Al 2021

“LSSL: Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers”

“Recurrent Model-Free RL Is a Strong Baseline for Many POMDPs”, Ni Et Al 2021

“Recurrent Model-Free RL is a Strong Baseline for Many POMDPs”

“Photos Are All You Need for Reciprocal Recommendation in Online Dating”, Neve & McConville2021

“Photos Are All You Need for Reciprocal Recommendation in Online Dating”

“Perceiver IO: A General Architecture for Structured Inputs & Outputs”, Jaegle Et Al 2021

“Perceiver IO: A General Architecture for Structured Inputs & Outputs”

“Unbiased Gradient Estimation in Unrolled Computation Graphs With Persistent Evolution Strategies”, Vicol Et Al 2021

“Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies”

“Shelley: A Crowd-sourced Collaborative Horror Writer”, Delul Et Al 2021

“Shelley: A Crowd-sourced Collaborative Horror Writer”

“Ten Lessons From Three Generations Shaped Google’s TPUv4i”, Jouppi Et Al 2021

“Ten Lessons From Three Generations Shaped Google’s TPUv4i”

“RASP: Thinking Like Transformers”, Weiss Et Al 2021

“RASP: Thinking Like Transformers”

“Scaling Laws for Acoustic Models”, Droppo & Elibol2021

“Scaling Laws for Acoustic Models”

“Scaling End-to-End Models for Large-Scale Multilingual ASR”, Li Et Al 2021

“Scaling End-to-End Models for Large-Scale Multilingual ASR”

“Efficient Transformers in Reinforcement Learning Using Actor-Learner Distillation”, Parisotto & Salakhutdinov2021

“Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation”

“Finetuning Pretrained Transformers into RNNs”, Kasai Et Al 2021

“Finetuning Pretrained Transformers into RNNs”

“Pretrained Transformers As Universal Computation Engines”, Lu Et Al 2021

“Pretrained Transformers as Universal Computation Engines”

“Perceiver: General Perception With Iterative Attention”, Jaegle Et Al 2021

“Perceiver: General Perception with Iterative Attention”

“When Attention Meets Fast Recurrence: Training SRU++ Language Models With Reduced Compute”, Lei2021

“When Attention Meets Fast Recurrence: Training SRU++ Language Models with Reduced Compute”

“Predictive Coding Is a Consequence of Energy Efficiency in Recurrent Neural Networks”, Ali Et Al 2021

“Predictive coding is a consequence of energy efficiency in recurrent neural networks”

“Deep Residual Learning in Spiking Neural Networks”, Fang Et Al 2021

“Deep Residual Learning in Spiking Neural Networks”

“Distilling Large Language Models into Tiny and Effective Students Using PQRNN”, Kaliamoorthi Et Al 2021

“Distilling Large Language Models into Tiny and Effective Students using pQRNN”

“Meta Learning Backpropagation And Improving It”, Kirsch & Schmidhuber2020

“Meta Learning Backpropagation And Improving It”

“On the Binding Problem in Artificial Neural Networks”, Greff Et Al 2020

“On the Binding Problem in Artificial Neural Networks”

“A Recurrent Vision-and-Language BERT for Navigation”, Hong Et Al 2020

“A Recurrent Vision-and-Language BERT for Navigation”

“Towards Playing Full MOBA Games With Deep Reinforcement Learning”, Ye Et Al 2020

“Towards Playing Full MOBA Games with Deep Reinforcement Learning”

“Adversarial Vulnerabilities of Human Decision-making”, Dezfouli Et Al 2020

“Adversarial vulnerabilities of human decision-making”

“Learning to Summarize Long Texts With Memory Compression and Transfer”, Park Et Al 2020

“Learning to Summarize Long Texts with Memory Compression and Transfer”

“Human-centric Dialog Training via Offline Reinforcement Learning”, Jaques Et Al 2020

“Human-centric Dialog Training via Offline Reinforcement Learning”

“AFT: An Attention Free Transformer”, Anonymous2020

“AFT: An Attention Free Transformer”

“Deep Reinforcement Learning for Closed-Loop Blood Glucose Control”, Fox Et Al 2020

“Deep Reinforcement Learning for Closed-Loop Blood Glucose Control”

“HiPPO: Recurrent Memory With Optimal Polynomial Projections”, Gu Et Al 2020

“HiPPO: Recurrent Memory with Optimal Polynomial Projections”

“Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size”, Yoshida Et Al 2020

“Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size”

“Matt Botvinick on the Spontaneous Emergence of Learning Algorithms”, Scholl2020

“Matt Botvinick on the spontaneous emergence of learning algorithms”

“DeepSinger: Singing Voice Synthesis With Data Mined From the Web”, Ren Et Al 2020

“DeepSinger: Singing Voice Synthesis with Data Mined From the Web”

“High-performance Brain-to-text Communication via Imagined Handwriting”, Willett Et Al 2020

“High-performance brain-to-text communication via imagined handwriting”

“Transformers Are RNNs: Fast Autoregressive Transformers With Linear Attention”, Katharopoulos Et Al 2020

“Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention”

“The Recurrent Neural Tangent Kernel”, Alemohammad Et Al 2020

“The Recurrent Neural Tangent Kernel”

“Untangling Tradeoffs between Recurrence and Self-attention in Neural Networks”, Kerg Et Al 2020

“Untangling tradeoffs between recurrence and self-attention in neural networks”

“Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing”, Dai Et Al 2020

“Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing”

“Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models”, Papadimitriou & Jurafsky2020

“Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models”

“Syntactic Structure from Deep Learning”, Linzen & Baroni2020

“Syntactic Structure from Deep Learning”

“Agent57: Outperforming the Human Atari Benchmark”, Puigdomènech Et Al 2020

“Agent57: Outperforming the human Atari benchmark”

“Machine Translation of Cortical Activity to Text With an Encoder-decoder Framework”, Makin Et Al 2020

“Machine translation of cortical activity to text with an encoder-decoder framework”

“Learning-based Memory Allocation for C++ Server Workloads”, Maas Et Al 2020

“Learning-based Memory Allocation for C++ Server Workloads”

“Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving”, Song Et Al 2020

“Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving”

“Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks”, Hasson Et Al 2020

“Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks”

“Scaling Laws for Neural Language Models”, Kaplan Et Al 2020

“Scaling Laws for Neural Language Models”

“Placing Language in an Integrated Understanding System: Next Steps toward Human-level Performance in Neural Language Models”, McClelland Et Al 2020

“Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models”

“Estimating the Deep Replicability of Scientific Findings Using Human and Artificial Intelligence”, Yang Et Al 2020

“Estimating the deep replicability of scientific findings using human and artificial intelligence”

“Single Headed Attention RNN: Stop Thinking With Your Head”, Merity2019

“Single Headed Attention RNN: Stop Thinking With Your Head”

“Excavate”, Lynch2019

“MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser Et Al 2019

“MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model”

“CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning”, Lin Et Al 2019

“CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning”

“Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks”, Voelker Et Al 2019

“Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks”

“High Fidelity Video Prediction With Large Stochastic Recurrent Neural Networks”, Villegas Et Al 2019

“High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks”

“SEED RL: Scalable and Efficient Deep-RL With Accelerated Central Inference”, Espeholt Et Al 2019

“SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference”

“Mixed-Signal Neuromorphic Processors: Quo Vadis?”, Bavandpour Et Al 2019

“Mixed-Signal Neuromorphic Processors: Quo vadis?”

“R2D3: Making Efficient Use of Demonstrations to Solve Hard Exploration Problems”, Paine Et Al 2019

“R2D3: Making Efficient Use of Demonstrations to Solve Hard Exploration Problems”

“Language Modelling State-of-the-art Leaderboards”, Paperswithcode.com 2019

“Language Modelling State-of-the-art leaderboards”

“Metalearned Neural Memory”, Munkhdalai Et Al 2019

“Metalearned Neural Memory”

“Generating Text With Recurrent Neural Networks”, Sutskever Et Al 2019

“Generating Text with Recurrent Neural Networks”

“Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”, Socher Et Al 2019

“Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”

“XLNet: Generalized Autoregressive Pretraining for Language Understanding”, Yang Et Al 2019

“XLNet: Generalized Autoregressive Pretraining for Language Understanding”

“Playing the Lottery With Rewards and Multiple Languages: Lottery Tickets in RL and NLP”, Yu Et Al 2019

“Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP”

“Reinforcement Learning, Fast and Slow”, Botvinick Et Al 2019

“Reinforcement Learning, Fast and Slow”

“MoGlow: Probabilistic and Controllable Motion Synthesis Using Normalizing Flows”, Henter Et Al 2019

“MoGlow: Probabilistic and controllable motion synthesis using normalizing flows”

“Meta-learners’ Learning Dynamics Are unlike Learners’”, Rabinowitz2019

“Meta-learners’ learning dynamics are unlike learners’”

“Speech Synthesis from Neural Decoding of Spoken Sentences”, Anumanchipalli Et Al 2019

“Speech synthesis from neural decoding of spoken sentences”

“Good News, Everyone! Context Driven Entity-aware Captioning for News Images”, Biten Et Al 2019

“Good News, Everyone! Context driven entity-aware captioning for news images”

“Surrogate Gradient Learning in Spiking Neural Networks”, Neftci Et Al 2019

“Surrogate Gradient Learning in Spiking Neural Networks”

“Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”, Dai Et Al 2019

“Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”

“Natural Questions: A Benchmark for Question Answering Research”, Kwiatkowski Et Al 2019

“Natural Questions: A Benchmark for Question Answering Research”

“High Fidelity Video Prediction With Large Stochastic Recurrent Neural Networks: Videos”, Villegas Et Al 2019

“High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks: Videos”

“Bayesian Layers: A Module for Neural Network Uncertainty”, Tran Et Al 2018

“Bayesian Layers: A Module for Neural Network Uncertainty”

“Meta-Learning: Learning to Learn Fast”, Weng2018

“Meta-Learning: Learning to Learn Fast”

“Piano Genie”, Donahue Et Al 2018

“Piano Genie”

“Learning Recurrent Binary/Ternary Weights”, Ardakani Et Al 2018

“Learning Recurrent Binary/Ternary Weights”

“R2D2: Recurrent Experience Replay in Distributed Reinforcement Learning”, Kapturowski Et Al 2018

“R2D2: Recurrent Experience Replay in Distributed Reinforcement Learning”

“HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering”, Yang Et Al 2018

“HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering”

“Adversarial Reprogramming of Text Classification Neural Networks”, Neekhara Et Al 2018

“Adversarial Reprogramming of Text Classification Neural Networks”

“This Time With Feeling: Learning Expressive Musical Performance”, Oore Et Al 2018

“This Time with Feeling: Learning Expressive Musical Performance”

“Character-Level Language Modeling With Deeper Self-Attention”, Al-Rfou Et Al 2018

“Character-Level Language Modeling with Deeper Self-Attention”

“General Value Function Networks”, Schlegel Et Al 2018

“General Value Function Networks”

“Universal Transformers”, Dehghani Et Al 2018

“Universal Transformers”

“Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme”, Lau Et Al 2018

“Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme”

“Accurate Uncertainties for Deep Learning Using Calibrated Regression”, Kuleshov Et Al 2018

“Accurate Uncertainties for Deep Learning Using Calibrated Regression”

“Neural Ordinary Differential Equations”, Chen Et Al 2018

“Neural Ordinary Differential Equations”

“Know What You Don’t Know: Unanswerable Questions for SQuAD”, Rajpurkar Et Al 2018

“Know What You Don’t Know: Unanswerable Questions for SQuAD”

“Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data”, Yang Et Al 2018

“Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data”

“Hierarchical Neural Story Generation”, Fan Et Al 2018

“Hierarchical Neural Story Generation”

“Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context”, Khandelwal Et Al 2018

“Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context”

“Newsroom: A Dataset of 1.3 Million Summaries With Diverse Extractive Strategies”, Grusky Et Al 2018

“Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies”

“A Tree Search Algorithm for Sequence Labeling”, Lao Et Al 2018

“A Tree Search Algorithm for Sequence Labeling”

“An Analysis of Neural Language Modeling at Multiple Scales”, Merity Et Al 2018

“An Analysis of Neural Language Modeling at Multiple Scales”

“Reviving and Improving Recurrent Back-Propagation”, Liao Et Al 2018

“Reviving and Improving Recurrent Back-Propagation”

“Learning Memory Access Patterns”, Hashemi Et Al 2018

“Learning Memory Access Patterns”

“Learning Longer-term Dependencies in RNNs With Auxiliary Losses”, Trinh Et Al 2018

“Learning Longer-term Dependencies in RNNs with Auxiliary Losses”

“One Big Net For Everything”, Schmidhuber2018

“One Big Net For Everything”

“Efficient Neural Audio Synthesis”, Kalchbrenner Et Al 2018

“Efficient Neural Audio Synthesis”

“Deep Contextualized Word Representations”, Peters Et Al 2018

“Deep contextualized word representations”

“M-Walk: Learning to Walk over Graphs Using Monte Carlo Tree Search”, Shen Et Al 2018

“M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search”

“ULMFiT: Universal Language Model Fine-tuning for Text Classification”, Howard & Ruder2018

“ULMFiT: Universal Language Model Fine-tuning for Text Classification”

“Large-scale Comparison of Machine Learning Methods for Drug Target Prediction on ChEMBL”, Mayr Et Al 2018

“Large-scale comparison of machine learning methods for drug target prediction on ChEMBL”

“A Flexible Approach to Automated RNN Architecture Generation”, Schrimpf Et Al 2017

“A Flexible Approach to Automated RNN Architecture Generation”

“Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition”, Ye Et Al 2017

“Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition”

“Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”, Yang Et Al 2017

“Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”

“Evaluating Prose Style Transfer With the Bible”, Carlson Et Al 2017

“Evaluating prose style transfer with the Bible”

“Breaking the Softmax Bottleneck: A High-Rank RNN Language Model”, Yang Et Al 2017

“Breaking the Softmax Bottleneck: A High-Rank RNN Language Model”

“Neural Speed Reading via Skim-RNN”, Seo Et Al 2017

“Neural Speed Reading via Skim-RNN”

“Generalization without Systematicity: On the Compositional Skills of Sequence-to-sequence Recurrent Networks”, Lake & Baroni2017

“Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks”

“Mixed Precision Training”, Micikevicius Et Al 2017

“Mixed Precision Training”

“To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression”, Zhu & Gupta2017

“To prune, or not to prune: exploring the efficacy of pruning for model compression”

“N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning”, Ashok Et Al 2017

“N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning”

“Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”, Shim Et Al 2017

“Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”

“SRU: Simple Recurrent Units for Highly Parallelizable Recurrence”, Lei Et Al 2017

“SRU: Simple Recurrent Units for Highly Parallelizable Recurrence”

“Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”, Jayaraman & Grauman2017

“Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”

“Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks”, Campos Et Al 2017

“Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks”

“Twin Networks: Matching the Future for Sequence Generation”, Serdyuk Et Al 2017

“Twin Networks: Matching the Future for Sequence Generation”

“Revisiting Activation Regularization for Language RNNs”, Merity Et Al 2017

“Revisiting Activation Regularization for Language RNNs”

“Bayesian Sparsification of Recurrent Neural Networks”, Lobacheva Et Al 2017

“Bayesian Sparsification of Recurrent Neural Networks”

“On the State-of-the-Art of Evaluation in Neural Language Models”, Melis Et Al 2017

“On the State-of-the-Art of Evaluation in Neural Language Models”

“Controlling Linguistic Style Aspects in Neural Language Generation”, Ficler & Goldberg2017

“Controlling Linguistic Style Aspects in Neural Language Generation”

“Device Placement Optimization With Reinforcement Learning”, Mirhoseini Et Al 2017

“Device Placement Optimization with Reinforcement Learning”

“Language Generation With Recurrent Generative Adversarial Networks without Pre-training”, Press Et Al 2017

“Language Generation with Recurrent Generative Adversarial Networks without Pre-training”

“Biased Importance Sampling for Deep Neural Network Training”, Katharopoulos & Fleuret2017

“Biased Importance Sampling for Deep Neural Network Training”

“Deriving Neural Architectures from Sequence and Graph Kernels”, Lei Et Al 2017

“Deriving Neural Architectures from Sequence and Graph Kernels”

“A Deep Reinforced Model for Abstractive Summarization”, Paulus Et Al 2017

“A Deep Reinforced Model for Abstractive Summarization”

“DeepTingle”, Khalifa Et Al 2017

“DeepTingle”

“TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension”, Joshi Et Al 2017

“TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension”

“A Neural Network System for Transformation of Regional Cuisine Style”, Kazama Et Al 2017

“A neural network system for transformation of regional cuisine style”

“Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPU”, Devlin2017

“Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPU”

“Adversarial Neural Machine Translation”, Wu Et Al 2017

“Adversarial Neural Machine Translation”

“Learning to Reason: End-to-End Module Networks for Visual Question Answering”, Hu Et Al 2017

“Learning to Reason: End-to-End Module Networks for Visual Question Answering”

“SearchQA: A New Q&A Dataset Augmented With Context from a Search Engine”, Dunn Et Al 2017

“SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine”

“Exploring Sparsity in Recurrent Neural Networks”, Narang Et Al 2017

“Exploring Sparsity in Recurrent Neural Networks”

“DeepAR: Probabilistic Forecasting With Autoregressive Recurrent Networks”, Salinas Et Al 2017

“DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks”

“Recurrent Environment Simulators”, Chiappa Et Al 2017

“Recurrent Environment Simulators”

“Learning to Generate Reviews and Discovering Sentiment”, Radford Et Al 2017

“Learning to Generate Reviews and Discovering Sentiment”

“I2T2I: Learning Text to Image Synthesis With Textual Data Augmentation”, Dong Et Al 2017

“I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation”

“Improving Neural Machine Translation With Conditional Sequence Generative Adversarial Nets”, Yang Et Al 2017

“Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets”

“Learned Optimizers That Scale and Generalize”, Wichrowska Et Al 2017

“Learned Optimizers that Scale and Generalize”

“Parallel Multiscale Autoregressive Density Estimation”, Reed Et Al 2017

“Parallel Multiscale Autoregressive Density Estimation”

“Tracking the World State With Recurrent Entity Networks”, Henaff Et Al 2017

“Tracking the World State with Recurrent Entity Networks”

“Optimization As a Model for Few-Shot Learning”, Ravi & Larochelle2017

“Optimization as a Model for Few-Shot Learning”

“Neural Combinatorial Optimization With Reinforcement Learning”, Bello Et Al 2017

“Neural Combinatorial Optimization with Reinforcement Learning”

“Tuning Recurrent Neural Networks With Reinforcement Learning”, Jaques Et Al 2017

“Tuning Recurrent Neural Networks with Reinforcement Learning”

“Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer”, Shazeer Et Al 2017

“Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer”

“Neural Data Filter for Bootstrapping Stochastic Gradient Descent”, Fan Et Al 2017

“Neural Data Filter for Bootstrapping Stochastic Gradient Descent”

“SampleRNN: An Unconditional End-to-End Neural Audio Generation Model”, Mehri Et Al 2016

“SampleRNN: An Unconditional End-to-End Neural Audio Generation Model”

“Improving Neural Language Models With a Continuous Cache”, Grave Et Al 2016

“Improving Neural Language Models with a Continuous Cache”

“NewsQA: A Machine Comprehension Dataset”, Trischler Et Al 2016

“NewsQA: A Machine Comprehension Dataset”

“Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation”, Johnson Et Al 2016

“Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation”

“Learning to Learn without Gradient Descent by Gradient Descent”, Chen Et Al 2016

“Learning to Learn without Gradient Descent by Gradient Descent”

“RL²: Fast Reinforcement Learning via Slow Reinforcement Learning”, Duan Et Al 2016

“RL²: Fast Reinforcement Learning via Slow Reinforcement Learning”

“DeepCoder: Learning to Write Programs”, Balog Et Al 2016

“DeepCoder: Learning to Write Programs”

“Bidirectional Attention Flow for Machine Comprehension”, Seo Et Al 2016

“Bidirectional Attention Flow for Machine Comprehension”

“Neural Architecture Search With Reinforcement Learning”, Zoph & Le2016

“Neural Architecture Search with Reinforcement Learning”

“QRNNs: Quasi-Recurrent Neural Networks”, Bradbury Et Al 2016

“QRNNs: Quasi-Recurrent Neural Networks”

“Hybrid Computing Using a Neural Network With Dynamic External Memory”, Graves Et Al 2016

“Hybrid computing using a neural network with dynamic external memory”

“Using Fast Weights to Attend to the Recent Past”, Ba Et Al 2016

“Using Fast Weights to Attend to the Recent Past”

“Achieving Human Parity in Conversational Speech Recognition”, Xiong Et Al 2016

“Achieving Human Parity in Conversational Speech Recognition”

“HyperNetworks”, Ha Et Al 2016

“HyperNetworks”

“Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”, Wu Et Al 2016

“Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”

“Pointer Sentinel Mixture Models”, Merity Et Al 2016

“Pointer Sentinel Mixture Models”

“Deep Learning Human Mind for Automated Visual Classification”, Spampinato Et Al 2016

“Deep Learning Human Mind for Automated Visual Classification”

“Decoupled Neural Interfaces Using Synthetic Gradients”, Jaderberg Et Al 2016

“Decoupled Neural Interfaces using Synthetic Gradients”

“Full Resolution Image Compression With Recurrent Neural Networks”, Toderici Et Al 2016

“Full Resolution Image Compression with Recurrent Neural Networks”

“LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks”, Strobelt Et Al 2016

“LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks”

“Learning to Learn by Gradient Descent by Gradient Descent”, Andrychowicz Et Al 2016

“Learning to learn by gradient descent by gradient descent”

“Iterative Alternating Neural Attention for Machine Reading”, Sordoni Et Al 2016

“Iterative Alternating Neural Attention for Machine Reading”

“Deep Reinforcement Learning for Dialogue Generation”, Li Et Al 2016

“Deep Reinforcement Learning for Dialogue Generation”

“Programming With a Differentiable Forth Interpreter”, Bošnjak Et Al 2016

“Programming with a Differentiable Forth Interpreter”

“Training Deep Nets With Sublinear Memory Cost”, Chen Et Al 2016

“Training Deep Nets with Sublinear Memory Cost”

“Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex”, Liao & Poggio2016

“Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex”

“Improving Sentence Compression by Learning to Predict Gaze”, Klerke Et Al 2016

“Improving sentence compression by learning to predict gaze”

“Adaptive Computation Time for Recurrent Neural Networks”, Graves2016

“Adaptive Computation Time for Recurrent Neural Networks”

“Dynamic Memory Networks for Visual and Textual Question Answering”, Xiong Et Al 2016

“Dynamic Memory Networks for Visual and Textual Question Answering”

“PlaNet—Photo Geolocation With Convolutional Neural Networks”, Weyand Et Al 2016

“PlaNet—Photo Geolocation with Convolutional Neural Networks”

“Learning Distributed Representations of Sentences from Unlabeled Data”, Hill Et Al 2016

“Learning Distributed Representations of Sentences from Unlabeled Data”

“Exploring the Limits of Language Modeling”, Jozefowicz Et Al 2016

“Exploring the Limits of Language Modeling”

“Pixel Recurrent Neural Networks”, Oord Et Al 2016

“Pixel Recurrent Neural Networks”

“Persistent RNNs: Stashing Recurrent Weights On-Chip”, Diamos Et Al 2016

“Persistent RNNs: Stashing Recurrent Weights On-Chip”

“Deep-Spying: Spying Using Smartwatch and Deep Learning”, Beltramelli & Risi2015

“Deep-Spying: Spying using Smartwatch and Deep Learning”

“On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, Schmidhuber2015

“On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”

“Neural GPUs Learn Algorithms”, Kaiser & Sutskever2015

“Neural GPUs Learn Algorithms”

“Sequence Level Training With Recurrent Neural Networks”, Ranzato Et Al 2015

“Sequence Level Training with Recurrent Neural Networks”

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“Generating Sentences from a Continuous Space”

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“Generative Concatenative Nets Jointly Learn to Write and Classify Reviews”

“Generating Images from Captions With Attention”, Mansimov Et Al 2015

“Generating Images from Captions with Attention”

“Semi-supervised Sequence Learning”, Dai & Le2015

“Semi-supervised Sequence Learning”

“RNN Metadata for Mimicking Author Style”, Gwern 2015

“RNN Metadata for Mimicking Author Style”

“Deep Recurrent Q-Learning for Partially Observable MDPs”, Hausknecht & Stone2015

“Deep Recurrent Q-Learning for Partially Observable MDPs”

“Teaching Machines to Read and Comprehend”, Hermann Et Al 2015

“Teaching Machines to Read and Comprehend”

“Scheduled Sampling for Sequence Prediction With Recurrent Neural Networks”, Bengio Et Al 2015

“Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks”

“Visualizing and Understanding Recurrent Networks”, Karpathy Et Al 2015

“Visualizing and Understanding Recurrent Networks”

“The Unreasonable Effectiveness of Recurrent Neural Networks”, Karpathy2015

“The Unreasonable Effectiveness of Recurrent Neural Networks”

“Deep Neural Networks for Large Vocabulary Handwritten Text Recognition”, Bluche2015

“Deep Neural Networks for Large Vocabulary Handwritten Text Recognition”

“Reinforcement Learning Neural Turing Machines—Revised”, Zaremba & Sutskever2015

“Reinforcement Learning Neural Turing Machines—Revised”

“End-To-End Memory Networks”, Sukhbaatar Et Al 2015

“End-To-End Memory Networks”

“Inferring Algorithmic Patterns With Stack-Augmented Recurrent Nets”, Arm Et Al 2015

“Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets”

“DRAW: A Recurrent Neural Network For Image Generation”, Gregor Et Al 2015

“DRAW: A Recurrent Neural Network For Image Generation”

“Neural Turing Machines”, Graves Et Al 2014

“Neural Turing Machines”

“Learning to Execute”, Zaremba & Sutskever2014

“Learning to Execute”

“Neural Machine Translation by Jointly Learning to Align and Translate”, Bahdanau Et Al 2014

“Neural Machine Translation by Jointly Learning to Align and Translate”

“Distributed Representations of Sentences and Documents”, Le & Mikolov2014

“Distributed Representations of Sentences and Documents”

“One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling”, Chelba Et Al 2013

“One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling”

“Generating Sequences With Recurrent Neural Networks”, Graves2013

“Generating Sequences With Recurrent Neural Networks”

“Recurrent Neural Network Based Language Model”, Mikolov Et Al 2010

“Recurrent Neural Network Based Language Model”

“Large Language Models in Machine Translation”, Brants Et Al 2007

“Large Language Models in Machine Translation”

“Learning to Learn Using Gradient Descent”, Hochreiter Et Al 2001

“Learning to Learn Using Gradient Descent”

“Long Short-Term Memory”, Hochreiter & Schmidhuber1997

“Long Short-Term Memory”

“Flat Minima”, Hochreiter & Schmidhuber1997

“Flat Minima”

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“Gradient-Based Learning Algorithms for Recurrent Networks and Their Computational Complexity”

“A Focused Backpropagation Algorithm for Temporal Pattern Recognition”, Mozer1995

“A Focused Backpropagation Algorithm for Temporal Pattern Recognition”

“Learning Complex, Extended Sequences Using the Principle of History Compression”, Schmidhuber1992

“Learning Complex, Extended Sequences Using the Principle of History Compression”

“Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks”, Schmidhuber1992

“Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks”

“Untersuchungen Zu Dynamischen Neuronalen Netzen [Studies of Dynamic Neural Networks]”, Hochreiter1991

“Untersuchungen zu dynamischen neuronalen Netzen [Studies of dynamic neural networks]”

“Finding Structure In Time”, Elman1990

“Finding Structure In Time”

“Connectionist Music Composition Based on Melodic, Stylistic, and Psychophysical Constraints [Technical Report CU-CS–495–90]”, Mozer1990

“Connectionist Music Composition Based on Melodic, Stylistic, and Psychophysical Constraints [Technical report CU-CS–495–90]”

“A Learning Algorithm for Continually Running Fully Recurrent Neural Networks”, Williams & Zipser1989b

“A Learning Algorithm for Continually Running Fully Recurrent Neural Networks”

“A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks”, Schmidhuber1989

“A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks”

“Experimental Analysis of the Real-time Recurrent Learning Algorithm”, Williams & Zipser1989

“Experimental Analysis of the Real-time Recurrent Learning Algorithm”

“A Sticky-Bit Approach for Learning to Represent State”, Bachrach1988

“A Sticky-Bit Approach for Learning to Represent State”

“The Utility Driven Dynamic Error Propagation Network (RTRL)”, Robinson & Fallside1987

“The Utility Driven Dynamic Error Propagation Network (RTRL)”

“Serial Order: A Parallel Distributed Processing Approach”, Jordan1986

“Serial Order: A Parallel Distributed Processing Approach”

“Attention and Augmented Recurrent Neural Networks”

“Attention and Augmented Recurrent Neural Networks”

“Deep Learning for Assisting the Process of Music Composition (part 3)”

“Deep learning for assisting the process of music composition (part 3)”

Wikipedia

Miscellaneous

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