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04a3288 Feb 12, 2018
@dennybritz @ReDeiPirati @benjisympa @SeanRosario @RetrieverJo
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2018-02

  • The Matrix Calculus You Need For Deep Learning [arXiv]
  • Regularized Evolution for Image Classifier Architecture Search [arXiv]
  • Online Learning: A Comprehensive Survey [arXiv]
  • Visual Interpretability for Deep Learning: a Survey [arXiv]
  • Behavior is Everything – Towards Representing Concepts with Sensorimotor Contingencies [paper] [article] [code]
  • IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures [arXiv] [article] [code]
  • DeepType: Multilingual Entity Linking by Neural Type System Evolution [arXiv] [article] [code]
  • DensePose: Dense Human Pose Estimation In The Wild [arXiv] [article]

2018-01

  • Nested LSTMs [arXiv]
  • Generating Wikipedia by Summarizing Long Sequences [arXiv]
  • Scalable and accurate deep learning for electronic health records [arXiv]
  • Kernel Feature Selection via Conditional Covariance Minimization [NIPS paper] [article] [code]
  • Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents [arXiv] [article] [code]
  • Fine-tuned Language Models for Text Classification [arXiv] [code] (soon)
  • Deep Learning: An Introduction for Applied Mathematicians [arXiv]
  • Innateness, AlphaZero, and Artificial Intelligence [arXiv]
  • Can Computers Create Art? [arXiv]
  • eCommerceGAN : A Generative Adversarial Network for E-commerce [arXiv]
  • Expected Policy Gradients for Reinforcement Learning [arXiv]
  • DroNet: Learning to Fly by Driving [UZH docs] [article] [code]
  • Symmetric Decomposition of Asymmetric Games [Scientific Reports] [article]
  • Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor [arXiv] [code]
  • SBNet: Sparse Blocks Network for Fast Inference [arXiv] [article] [code]
  • DeepMind Control Suite [arXiv] [code]
  • Deep Learning: A Critical Appraisal [arXiv]

2017-12

  • Adversarial Patch [arXiv]
  • CNN Is All You Need [arXiv]
  • Learning Robot Objectives from Physical Human Interaction [paper] [article]
  • The NarrativeQA Reading Comprehension Challenge [arXiv] [dataset]
  • Objects that Sound [arXiv]
  • Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions [arXiv] [article] [article2]
  • Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning [arXiv] [article] [code]
  • Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents [arXiv] [article] [code]
  • Superhuman AI for heads-up no-limit poker: Libratus beats top professionals [Science]
  • Mathematics of Deep Learning [arXiv]
  • State-of-the-art Speech Recognition With Sequence-to-Sequence Models [arXiv] [article]
  • Peephole: Predicting Network Performance Before Training [arXiv]
  • Deliberation Network: Pushing the frontiers of neural machine translation [Research at Microsoft] [article]
  • GPU Kernels for Block-Sparse Weights [Research at OpenAI] [article] [code]
  • Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm [arXiv]
  • Deep Learning Scaling is Predictable, Empirically [arXiv] [article]

2017-11

  • High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs [arXiv] [article] [code]
  • StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation [arXiv] [code]
  • Population Based Training of Neural Networks [arXiv] [article]
  • Distilling a Neural Network Into a Soft Decision Tree [arXiv]
  • Neural Text Generation: A Practical Guide [arXiv]
  • Parallel WaveNet: Fast High-Fidelity Speech Synthesis [DeepMind documents] [article]
  • CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning [arXiv] [article]
  • Non-local Neural Networks [arXiv]
  • Deep Image Prior [paper] [article] [code]
  • Online Deep Learning: Learning Deep Neural Networks on the Fly [arXiv]
  • Learning Explanatory Rules from Noisy Data [arXiv]
  • Improving Palliative Care with Deep Learning [arXiv] [article]
  • VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [arXiv]
  • Weighted Transformer Network for Machine Translation [arXiv] [article]
  • Non-Autoregressive Neural Machine Translation [arXiv] [article]
  • Block-Sparse Recurrent Neural Networks [arXiv]
  • A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning [arXiv]
  • Neural Discrete Representation Learning [arXiv] [article]
  • Don't Decay the Learning Rate, Increase the Batch Size [arXiv]
  • Hierarchical Representations for Efficient Architecture Search [arXiv]

2017-10

  • Unsupervised Machine Translation Using Monolingual Corpora Only [arXiv]
  • Dynamic Routing Between Capsules [arXiv]
  • A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs [Science] [article] [code]
  • Understanding Grounded Language Learning Agents [arXiv]
  • Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning [arXiv] [article] [code] (soon)
  • Malware Detection by Eating a Whole EXE [arXiv] [article]
  • Progressive Growing of GANs for Improved Quality, Stability, and Variation [Research at Nvidia] [article] [code]
  • Meta Learning Shared Hierarchies [arXiv] [article] [code]
  • Deep Voice 3: 2000-Speaker Neural Text-to-Speech [arXiv] [article]
  • AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions [arXiv] [article] [dataset]
  • Mastering the game of Go without Human Knowledge [Nature] [article]
  • Sim-to-Real Transfer of Robotic Control with Dynamics Randomization [arXiv] [article]
  • Asymmetric Actor Critic for Image-Based Robot Learning [arXiv] [article]
  • A systematic study of the class imbalance problem in convolutional neural networks [arXiv]
  • Generalization in Deep Learning [arXiv]
  • Swish: a Self-Gated Activation Function [arXiv]
  • Emergent Translation in Multi-Agent Communication [arXiv]
  • SLING: A framework for frame semantic parsing [arXiv] [article] [code]
  • Meta-Learning for Wrestling [arXiv] [article] [code]
  • Mixed Precision Training [arXiv] [article] [article2] [code/docs]
  • Generative Adversarial Networks: An Overview [arXiv]
  • Emergent Complexity via Multi-Agent Competition [arXiv] [article] [code]
  • Deep Lattice Networks and Partial Monotonic Functions [Research at Google] [article] [code]
  • The IIT Bombay English-Hindi Parallel Corpus [arXiv] [article]
  • Rainbow: Combining Improvements in Deep Reinforcement Learning [arXiv]
  • Lifelong Learning With Dynamically Expandable Networks [arXiv]
  • Variational Inference & Deep Learning: A New Synthesis (Thesis) [dropbox]
  • Neural Task Programming: Learning to Generalize Across Hierarchical Tasks [arXiv]
  • Neural Color Transfer between Images [arXiv]
  • The hippocampus as a predictive map [biorXiv] [article]
  • Scalable and accurate deep learning for electronic health records [arXiv]

2017-09

  • Variational Memory Addressing in Generative Models [arXiv]
  • Overcoming Exploration in Reinforcement Learning with Demonstrations [arXiv]
  • A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement [arXiv] [article] [code]
  • ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases [CVF] [article] [dataset]
  • NIMA: Neural Image Assessment [arXiv] [article]
  • Generating Sentences by Editing Prototypes [arXiv] [code]
  • The Consciousness Prior [arXiv]
  • StarSpace: Embed All The Things! [arXiv] [code]
  • Neural Optimizer Search with Reinforcement Learning [arXiv]
  • Dynamic Evaluation of Neural Sequence Models [arXiv]
  • Neural Machine Translation [arXiv]
  • Matterport3D: Learning from RGB-D Data in Indoor Environments [arXiv] [article] [article2] [code]
  • Deep Reinforcement Learning that Matters [arXiv] [code]
  • The Uncertainty Bellman Equation and Exploration [arXiv]
  • WESPE: Weakly Supervised Photo Enhancer for Digital Cameras [arXiv] [article]
  • Globally Normalized Reader [arXiv] [article] [code]
  • A Brief Introduction to Machine Learning for Engineers [arXiv]
  • Learning with Opponent-Learning Awareness [arXiv] [article]
  • A Deep Reinforcement Learning Chatbot [arXiv]
  • Squeeze-and-Excitation Networks [arXiv]
  • Efficient Methods and Hardware for Deep Learning (Thesis) [Stanford Digital Repository]

2017-08

  • Design and Analysis of the NIPS 2016 Review Process [arXiv]
  • Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks [arXiv] [article]
  • TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow [white paper] [code]
  • Automated Crowdturfing Attacks and Defenses in Online Review Systems [arXiv]
  • Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning [arXiv] [article] [code]
  • Deep Learning for Video Game Playing [arXiv]
  • Deep & Cross Network for Ad Click Predictions [arXiv]
  • Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms [arXiv] [code]
  • Multi-task Self-Supervised Visual Learning [arXiv]
  • Learning a Multi-View Stereo Machine [arXiv] [article] [code] (soon)
  • Twin Networks: Using the Future as a Regularizer [arXiv]
  • A Brief Survey of Deep Reinforcement Learning [arXiv]
  • Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation [arXiv] [code]
  • On the Effectiveness of Visible Watermarks [CVPR] [article]
  • Practical Network Blocks Design with Q-Learning [arXiv]
  • On Ensuring that Intelligent Machines Are Well-Behaved [arXiv]
  • Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control [arXiv] [code]
  • Training Deep AutoEncoders for Collaborative Filtering [arXiv] [code]
  • Learning to Perform a Perched Landing on the GroundUsing Deep Reinforcement Learning [nature]
  • Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification [arXiv] [article]
  • Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning [arXiv]
  • Neural Expectation Maximization [arXiv] [code]
  • Google Vizier: A Service for Black-Box Optimization [Research at Google]
  • STARDATA: A StarCraft AI Research Dataset [arXiv] [code]
  • Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm [arXiv] [code] [article]
  • Natural Language Processing with Small Feed-Forward Networks [arXiv]

2017-07

  • Photographic Image Synthesis with Cascaded Refinement Networks [arXiv] [code]
  • StarCraft II: A New Challenge for Reinforcement Learning [DeepMind Documents] [code] [article]
  • Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards [arXiv]
  • Reinforcement Learning with Deep Energy-Based Policies [arXiv] [article] [code]
  • DARLA: Improving Zero-Shot Transfer in Reinforcement Learning [arXiv]
  • Synthesizing Robust Adversarial Examples [arXiv] [article] [code] (Soon)
  • Voice Synthesis for in-the-Wild Speakers via a Phonological Loop [arXiv] [code] [article]
  • Eyemotion: Classifying facial expressions in VR using eye-tracking cameras [arXiv] [article]
  • A Distributional Perspective on Reinforcement Learning [arXiv] [article] [video]
  • On the State of the Art of Evaluation in Neural Language Models [arXiv]
  • Optimizing the Latent Space of Generative Networks [arXiv]
  • Neuroscience-Inspired Artificial Intelligence [Neuron] [article]
  • Learning Transferable Architectures for Scalable Image Recognition [arXiv]
  • Reverse Curriculum Generation for Reinforcement Learning [arXiv]
  • Imagination-Augmented Agents for Deep Reinforcement Learning [arXiv] [article]
  • Learning model-based planning from scratch [arXiv] [article]
  • Proximal Policy Optimization Algorithms [AWSS3] [code]
  • Automatic Recognition of Deceptive Facial Expressions of Emotion [arXiv]
  • Distral: Robust Multitask Reinforcement Learning [arXiv]
  • Creatism: A deep-learning photographer capable of creating professional work [arXiv] [article]
  • SCAN: Learning Abstract Hierarchical Compositional Visual Concepts [arXiv] [article]
  • Revisiting Unreasonable Effectiveness of Data in Deep Learning Era [arXiv] [article]
  • The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously [arXiv]
  • Deep Bilateral Learning for Real-Time Image Enhancement [arXiv] [code] [article]
  • Emergence of Locomotion Behaviours in Rich Environments [arXiv] [article]
  • Learning human behaviors from motion capture by adversarial imitation [arXiv] [article]
  • Robust Imitation of Diverse Behaviors [arXiv] [article]
  • Hindsight Experience Replay [arXiv]
  • Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks [arXiv] [article]
  • End-to-End Learning of Semantic Grasping [arXiv]
  • ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games [arXiv] [code] [article]

2017-06

  • Noisy Networks for Exploration [arXiv]
  • Do GANs actually learn the distribution? An empirical study [arXiv]
  • Gradient Episodic Memory for Continuum Learning [arXiv]
  • Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog [arXiv] [code]
  • Deep Interest Network for Click-Through Rate Prediction [arXiv]
  • Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study [arXiv] [article]
  • Structure Learning in Motor Control: A Deep Reinforcement Learning Model [arXiv]
  • Programmable Agents [arXiv]
  • Grounded Language Learning in a Simulated 3D World [arXiv]
  • Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics [arXiv]
  • SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability [arXiv] [article] [code]
  • One Model To Learn Them All [arXiv] [code] [article]
  • Hybrid Reward Architecture for Reinforcement Learning [arXiv]
  • Expected Policy Gradients [arXiv]
  • Variational Approaches for Auto-Encoding Generative Adversarial Networks [arXiv]
  • Deal or No Deal? End-to-End Learning for Negotiation Dialogues [S3AWS] [code] [article]
  • Attention Is All You Need [arXiv] [code] [article]
  • Sobolev Training for Neural Networks [arXiv]
  • YellowFin and the Art of Momentum Tuning [arXiv] [code] [article]
  • Forward Thinking: Building and Training Neural Networks One Layer at a Time [arXiv]
  • Depthwise Separable Convolutions for Neural Machine Translation [arXiv] [code]
  • Parameter Space Noise for Exploration [arXiv] [code] [article]
  • Deep Reinforcement Learning from human preferences [arXiv] [article]
  • Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments [arXiv] [code]
  • Self-Normalizing Neural Networks [arXiv] [code]
  • Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour [arXiv]
  • A simple neural network module for relational reasoning [arXiv] [article]
  • Visual Interaction Networks [arXiv] [article]

2017-05

  • Supervised Learning of Universal Sentence Representations from Natural Language Inference Data [arXiv] [code]
  • pix2code: Generating Code from a Graphical User Interface Screenshot [arXiv] [article] [code]
  • The Cramer Distance as a Solution to Biased Wasserstein Gradients [arXiv]
  • Reinforcement Learning with a Corrupted Reward Channel [arXiv]
  • Dilated Residual Networks [arXiv] [code]
  • Bayesian GAN [arXiv] [code]
  • Gradient Descent Can Take Exponential Time to Escape Saddle Points [arXiv] [article]
  • Thinking Fast and Slow with Deep Learning and Tree Search [arXiv]
  • ParlAI: A Dialog Research Software Platform [arXiv] [code] [article]
  • Semantically Decomposing the Latent Spaces of Generative Adversarial Networks [arXiv] [article]
  • Look, Listen and Learn [arXiv]
  • Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset [arXiv] [code]
  • Convolutional Sequence to Sequence Learning [arXiv] [code] [code2] [article]
  • The Kinetics Human Action Video Dataset [arXiv] [article]
  • Safe and Nested Subgame Solving for Imperfect-Information Games [arXiv]
  • Discrete Sequential Prediction of Continuous Actions for Deep RL [arXiv]
  • Metacontrol for Adaptive Imagination-Based Optimization [arXiv]
  • Efficient Parallel Methods for Deep Reinforcement Learning [arXiv]
  • Real-Time Adaptive Image Compression [arXiv]

2017-04

  • General Video Game AI: Learning from Screen Capture [arXiv]
  • Learning to Skim Text [arXiv]
  • Get To The Point: Summarization with Pointer-Generator Networks [arXiv] [code] [article]
  • Adversarial Neural Machine Translation [arXiv]
  • Deep Q-learning from Demonstrations [arXiv]
  • Learning from Demonstrations for Real World Reinforcement Learning [arXiv]
  • DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks [arXiv] [article] [code]
  • A Neural Representation of Sketch Drawings [arXiv] [code] [article]
  • Automated Curriculum Learning for Neural Networks [arXiv]
  • Hierarchical Surface Prediction for 3D Object Reconstruction [arXiv] [article]
  • Neural Message Passing for Quantum Chemistry [arXiv]
  • Learning to Generate Reviews and Discovering Sentiment [arXiv] [code]
  • Best Practices for Applying Deep Learning to Novel Applications [arXiv]

2017-03

  • Improved Training of Wasserstein GANs [arXiv]
  • Evolution Strategies as a Scalable Alternative to Reinforcement Learning [arXiv]
  • Controllable Text Generation [arXiv]
  • Neural Episodic Control [arXiv]
  • A Structured Self-attentive Sentence Embedding [arXiv]
  • Multi-step Reinforcement Learning: A Unifying Algorithm [arXiv]
  • Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG [arXiv]
  • FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning [arXiv] [article] [article2]
  • Massive Exploration of Neural Machine Translation Architectures [arXiv] [code]
  • Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression [arXiv] [article] [code]
  • Minimax Regret Bounds for Reinforcement Learning [arXiv]
  • Sharp Minima Can Generalize For Deep Nets [arXiv]
  • Parallel Multiscale Autoregressive Density Estimation [arXiv]
  • Neural Machine Translation and Sequence-to-sequence Models: A Tutorial [arXiv]
  • Large-Scale Evolution of Image Classifiers [arXiv]
  • FeUdal Networks for Hierarchical Reinforcement Learning [arXiv]
  • Evolving Deep Neural Networks [arXiv]
  • How to Escape Saddle Points Efficiently [arXiv] [article]
  • Opening the Black Box of Deep Neural Networks via Information [arXiv] [video]
  • Understanding Synthetic Gradients and Decoupled Neural Interfaces [arXiv]
  • Learning to Optimize Neural Nets [arXiv] [article]

2017-02

  • The Shattered Gradients Problem: If resnets are the answer, then what is the question? [arXiv]
  • Neural Map: Structured Memory for Deep Reinforcement Learning [arXiv]
  • Bridging the Gap Between Value and Policy Based Reinforcement Learning [arXiv]
  • Deep Voice: Real-time Neural Text-to-Speech [arXiv]
  • Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning [arXiv]
  • The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI [arXiv]
  • Learning to Parse and Translate Improves Neural Machine Translation [arXiv]
  • All-but-the-Top: Simple and Effective Postprocessing for Word Representations [arXiv]
  • Deep Learning with Dynamic Computation Graphs [arXiv]
  • Skip Connections as Effective Symmetry-Breaking [arXiv]
  • odelSemi-Supervised QA with Generative Domain-Adaptive Nets [arXiv]

2017-01

  • Wasserstein GAN [arXiv]
  • Deep Reinforcement Learning: An Overview [arXiv]
  • DyNet: The Dynamic Neural Network Toolkit [arXiv]
  • DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker [arXiv]
  • NIPS 2016 Tutorial: Generative Adversarial Networks [arXiv]

2016-12

  • A recurrent neural network without Chaos [arXiv]
  • Language Modeling with Gated Convolutional Networks [arXiv]
  • EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis [arXiv] [article]
  • Learning from Simulated and Unsupervised Images through Adversarial Training [arXiv]
  • How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs [arXiv]
  • Improving Neural Language Models with a Continuous Cache [arXiv]
  • DeepMind Lab [arXiv] [code]
  • Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision [arXiv]
  • Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning [arXiv]
  • Overcoming catastrophic forgetting in neural networks [arXiv]

2016-11 (ICLR Edition)

Reinforcement Learning:

-Learning to reinforcement learn [arXiv]

Machine Translation & Dialog

2016-10

2016-09

  • Towards Deep Symbolic Reinforcement Learning [arXiv]
  • HyperNetworks [arXiv]
  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation [arXiv]
  • Safe and Efficient Off-Policy Reinforcement Learning [arXiv]
  • Playing FPS Games with Deep Reinforcement Learning [arXiv]
  • SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient [arXiv]
  • Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks [arXiv]
  • Energy-based Generative Adversarial Network [arXiv]
  • Stealing Machine Learning Models via Prediction APIs [arXiv]
  • Semi-Supervised Classification with Graph Convolutional Networks [arXiv]
  • WaveNet: A Generative Model For Raw Audio [arXiv]
  • Hierarchical Multiscale Recurrent Neural Networks [arXiv]
  • End-to-End Reinforcement Learning of Dialogue Agents for Information Access [arXiv]
  • Deep Neural Networks for YouTube Recommendations [paper]

2016-08

  • Semantics derived automatically from language corpora contain human-like biases [arXiv]
  • Why does deep and cheap learning work so well? [arXiv]
  • Machine Comprehension Using Match-LSTM and Answer Pointer [arXiv]
  • Stacked Approximated Regression Machine: A Simple Deep Learning Approach [arXiv]
  • Decoupled Neural Interfaces using Synthetic Gradients [arXiv]
  • WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia [arXiv]
  • Temporal Attention Model for Neural Machine Translation [arXiv]
  • Residual Networks of Residual Networks: Multilevel Residual Networks [arXiv]
  • Learning Online Alignments with Continuous Rewards Policy Gradient [arXiv]

2016-07

2016-06

2016-05

  • Hierarchical Memory Networks [arXiv]
  • Deep API Learning [arXiv]
  • Wide Residual Networks [arXiv]
  • TensorFlow: A system for large-scale machine learning [arXiv]
  • Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention [arXiv]
  • Aspect Level Sentiment Classification with Deep Memory Network [arXiv]
  • FractalNet: Ultra-Deep Neural Networks without Residuals [arXiv]
  • Learning End-to-End Goal-Oriented Dialog [arXiv]
  • One-shot Learning with Memory-Augmented Neural Networks [arXiv]
  • Deep Learning without Poor Local Minima [arXiv]
  • AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge [arXiv]
  • Data Programming: Creating Large Training Sets, Quickly [arXiv]
  • Deeply-Fused Nets [arXiv]
  • Deep Portfolio Theory [arXiv]
  • Unsupervised Learning for Physical Interaction through Video Prediction [arXiv]
  • Movie Description [arXiv]

2016-04

2016-03

2016-02

2016-01

2015-12

NLP

Vision

2015-11

NLP

Programs

  • Neural Random-Access Machines [arxiv]
  • Neural Programmer: Inducing Latent Programs with Gradient Descent [arXiv]
  • Neural Programmer-Interpreters [arXiv]
  • Learning Simple Algorithms from Examples [arXiv]
  • Neural GPUs Learn Algorithms [arXiv] [code]
  • On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models [arXiv]

Vision

  • ReSeg: A Recurrent Neural Network for Object Segmentation [arXiv]
  • Deconstructing the Ladder Network Architecture [arXiv]
  • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [arXiv]
  • Multi-Scale Context Aggregation by Dilated Convolutions [arXiv] [code]

General

2015-10

2015-09

2015-08

2015-07

2015-06

2015-05

2015-04

  • Correlational Neural Networks [arXiv]

2015-03

2015-02

2015-01

  • Hidden Technical Debt in Machine Learning Systems [NIPS]

2014-12

2014-11

  • The Loss Surfaces of Multilayer Networks [arXiv]

2014-10

2014-09

2014-08

  • Convolutional Neural Networks for Sentence Classification [arxiv]

2014-07

2014-06

2014-05

2014-04

  • A Convolutional Neural Network for Modelling Sentences [arXiv]

2014-03

2014-02

2014-01

2013

  • Visualizing and Understanding Convolutional Networks [arXiv]
  • DeViSE: A Deep Visual-Semantic Embedding Model [pub]
  • Maxout Networks [arXiv]
  • Exploiting Similarities among Languages for Machine Translation [arXiv]
  • Efficient Estimation of Word Representations in Vector Space [arXiv]

2011

  • Natural Language Processing (almost) from Scratch [arXiv]