Artificial Intelligence
- A Roadmap towards Machine Intelligence
- Building Machines That Learn and Think Like People
- What Question Would Turing Pose Today?
Techniques
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training
- Advances In Optimizing Recurrent Networks
- Recurrent Neural Network Regularization
- Dynamic Capacity Networks
- Net2Net: Accelerating Learning via Knowledge Transfer
- Recurrent Batch Normalization
Computer Vision
Object Classification and Recognition
- ImageNet Classification with Deep Convolutional Neural Networks
- Going Deeper with Convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Visualizing and Understanding Convolutional Networks
- Intriguing Properties of Neural Networks
- Deep Residual Learning for Image Recognition
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Captioning, Style-Transfer and other extensions
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning
- Network in Network
- Residual Networks are Exponential Ensembles of Relatively Shallow Networks
- Residual Networks of Residual Networks: Multilevel Residual Networks
- Identity Mappings in Deep Residual Networks
- Learning Deep Features for Scene Recognition using Places Database
Image Generation
- DRAW: A Recurrent Neural Network For Image Generation
- Generative Adversarial Nets
- Conditional Generative Adversarial Nets
- Deep Convolutional Generative Adversarial Nets
- Conditional Image Generation with PixelCNN Decoders
- Pixel Recurrent Neural Network
Videos
- Delving Deeper into Convolutional Networks for Learning Video Representations
- Actions ~ Transformations
Visual Question Answering
Other
- You Only Look Once: Unified, Real-Time Object Detection
- Deep Visual Analogy-Making
- Object Detectors Emerge in Deep Scene CNNs
- Deep Networks with Stochastic Depth
- How transferable are features in deep neural networks?
Natural Language Processing
Embeddings
- GloVe: Global Vectors for Word Representation
- Improving Word Representations via Global Context and Multiple Word Prototypes
- Efficient Estimation of Word Representations in Vector Space
- Skip-Thought Vectors
Reading and comprehension
- WikiReading : A Novel Large-scale Language Understanding Task over Wikipedia
- Teaching Machines to Read and Comprehend
Translation
- Sequence to Sequence Learning with Neural Networks
- Neural Machine Translation by Jointly Learning to Align and Translate
- Fully Character-Level Neural Machine Translation without Explicit Segmentation
- Addressing the Rare Word Problem in Neural Machine Translation
- Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Question Answering
- WikiQA: A challenge dataset for open-domain question answering
- Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
- Question Answering with Subgraph Embeddings
Conversation / Dialog
- A Neural Conversational Model
- A Persona-Based Neural Conversation Model
- Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems
- Smart Reply: Automated Response Suggestion for Email
- Deep Reinforcement Learning for Dialogue Generation
Other
- Bag of Tricks for Efficient Text Classification (critique)
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
- Convolutional Neural Network For Sentence Classification
Reinforcement learning
- Human-Level Control Through Deep Reinforcement Learning
- Bridging the Gap Between Value and Policy Based Reinforcement Learning
- Deep Reinforcement Learning for Dialogue Generation
- [Technical Talk Notes] Deep Reinforcement Learning via Policy Optimization by John Schulman of OpenAI
- Dynamic Frame skip Deep Q Network
Code generation, or learning a computer program
- Learning To Execute
- Neural Turing Machines
- Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Visualization
Other
Last updated: 8th March, 2017