I am trying a new initiative - *A Paper A Week*. This blog will hold all the notes and summaries.

© 2019. All rights reserved.

- 2002 2
- 2014 4
- 2015 7
- 2016 9
- 2017 25
- 2018 27
- 2019 14
- AAAI 1
- AAAI 2018 1
- AAMAS 1
- AAMAS 2019 1
- ACL 4
- ACL 2015 1
- ACL 2016 1
- ACL 2017 1
- ACL2017 1
- AI 81
- Abductive Reasoning 1
- Abstract Summarization 1
- Accelerated Training 1
- Activation 1
- Activation Function 1
- Attention 4
- Benchmark 1
- CL 3
- CV 13
- CVPR 2
- CVPR 2016 1
- CVPR 2017 1
- Catastrophic Forgetting 4
- Causal Learning 1
- Causality 1
- Chemistry 1
- Clustering 1
- Compositionality 1
- Continual Learning 4
- Conversational Agent 1
- Count Based VQA 1
- Curriculum Learning 1
- DRL 4
- Data Augmentation 1
- Dataset 7
- Deep Reinforcement Learning 5
- Dependency Parsing 1
- Distributed Reinforcement Learning 1
- Dynamical System 1
- EMNLP 5
- EMNLP 2014 1
- EMNLP 2016 2
- EMNLP 2017 2
- Embedding 12
- Emergent Language 1
- Empirical Advice 2
- Entropy 1
- Environment 2
- Evaluating Generalization 1
- Evaluating Generalization' 1
- Evaluation 2
- Exploration 1
- Factorization 1
- GNN 5
- Generalization 2
- Graph 15
- Graph Neural Network 1
- Graph Representation 11
- Grounded Language Learning 1
- HRL 1
- Hierarchial RNN 1
- Hierarchical RL 1
- Hierarchical Reinforcement Learning 1
- Hyperbolic Embedding 2
- Hyperboloid Model 1
- Hypothesis 1
- ICCV 1
- ICCV 2015 1
- ICLR 13
- ICLR 2014 1
- ICLR 2015 1
- ICLR 2016 1
- ICLR 2018 3
- ICLR 2019 6
- ICLR 2019' 1
- ICLR2018 1
- ICML 9
- ICML 2016 1
- ICML 2017 1
- ICML 2018 5
- ICML 2019 3
- IRL 1
- Incremental Learning 1
- Information Retrieval 2
- Information Theory 1
- Initialization 1
- Interactive Teaching 1
- Inverse Reinforcement Learning 1
- KD 1
- KDD 2
- KDD 2015 1
- KDD 2017 1
- KRU 1
- Kernel 1
- Key Value 1
- Knowledge Distillation 1
- Knowledge Transfer 2
- Kronecker 1
- LR 1
- Latent Variable 1
- Learning Optimizer' 1
- Learning Rate 1
- Lifelong Learning 4
- Linear Algebra 1
- Linear Model 1
- Loss 2
- Loss Function 2
- MAML 1
- MANN 1
- MBRL 1
- MPNN 1
- Machine Comprehension 4
- Matrix 1
- Matrix Factorization 1
- Memory 3
- Memory Augmented Neural Network' 1
- Message Passing 1
- Meta Learning 5
- Meta Reinforcement Learning 1
- Meta-Learning 1
- Model-Based 2
- Model-Free 1
- Model-based 1
- Modular ML 1
- Modular Meta Learning 1
- Modular Network 1
- Module 1
- Motif 2
- Mujoco 1
- Multi Modal 2
- Multi Model 1
- Multi Task 2
- Multi-Agent 1
- NIPS 7
- NIPS 2014 2
- NIPS 2015 2
- NIPS 2017 3
- NIPS Workskop 1
- NLG 1
- NLI 1
- NLP 36
- NMT 1
- Natural Language Inference 2
- Natural Language Processing 8
- Network 3
- Network Embedding 1
- NeurIPS 1
- NeurIPS 2018 2
- NeurIPS Workshop 2018 1
- Neural Computation 1
- Neural Computation 2002 1
- Neural Message Passing 1
- Neural Module Network 1
- Neurips 2
- Neurips 2018 1
- Neurips 2019 1
- Off policy RL 1
- One shot learning 1
- Optimizer 1
- Out of Distribution 1
- Out of Vocabulary Words 1
- POS 1
- Physical Reasoning 1
- Physics 2
- Poincare Ball Model 2
- Pointer Network 1
- Pooling 1
- Pretraining 1
- Pruning Network 1
- QA 7
- RL 18
- RNN 4
- RRL 1
- Reasoning 2
- Recurrent Neural Network 2
- Reinforcement Learning 11
- Reinforcement Learning' 1
- Relation Learning 2
- Relational Inference 1
- Relational Learning 3
- Relational Network 1
- Representation Learning 3
- SAT 1
- SOTA 8
- Sample Efficient 1
- Science 2
- Science 2002 1
- Science 2016 1
- Self Gated 1
- Semantic Loss 1
- Sentiment Analysis 1
- Seq2Seq 1
- Sequential models 1
- Set 1
- Softmax 2
- Speech 1
- Structured Exploration 1
- Summarization 1
- Symbolic Knowledge 1
- Theory 1
- Transfer Learning 3
- Tree 1
- Tucker Decomposition 1
- Unsupervised 3
- VAE 1
- VQA 6
- Virtual Embodiment 1
- WACV 1
- WACV 2017 1
- Weight Adaptation 1
- Word Vectors 3
- Workshop 2

- » Multiple Model-Based Reinforcement Learning
- » Network Motifs - Simple Building Blocks of Complex Networks

- » An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
- » How transferable are features in deep neural networks
- » Distilling the Knowledge in a Neural Network
- » A Fast and Accurate Dependency Parser using Neural Networks

- » Exploring Models and Data for Image Question Answering
- » PTE - Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- » Word Representations via Gaussian Embedding
- » Pointer Networks
- » Two/Too Simple Adaptations of Word2Vec for Syntax Problems
- » Simple Baseline for Visual Question Answering
- » VQA-Visual Question Answering

- » One-shot Learning with Memory-Augmented Neural Networks
- » Net2Net-Accelerating Learning via Knowledge Transfer
- » Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
- » Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
- » Revisiting Semi-Supervised Learning with Graph Embeddings
- » Higher-order organization of complex networks
- » Ask Me Anything - Dynamic Memory Networks for Natural Language Processing
- » A Decomposable Attention Model for Natural Language Inference
- » Neural Module Networks

- » Linguistic Knowledge as Memory for Recurrent Neural Networks
- » Hindsight Experience Replay
- » Learned Optimizers that Scale and Generalize
- » Poincaré Embeddings for Learning Hierarchical Representations
- » HoME - a Household Multimodal Environment
- » Imagination-Augmented Agents for Deep Reinforcement Learning
- » Neural Message Passing for Quantum Chemistry
- » Unsupervised Learning by Predicting Noise
- » Cyclical Learning Rates for Training Neural Networks
- » Get To The Point - Summarization with Pointer-Generator Networks
- » StarSpace - Embed All The Things!
- » Emotional Chatting Machine - Emotional Conversation Generation with Internal and External Memory
- » Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
- » HARP - Hierarchical Representation Learning for Networks
- » Swish - a Self-Gated Activation Function
- » Reading Wikipedia to Answer Open-Domain Questions
- » Task-Oriented Query Reformulation with Reinforcement Learning
- » Refining Source Representations with Relation Networks for Neural Machine Translation
- » Learning to Compute Word Embeddings On the Fly
- » R-NET - Machine Reading Comprehension with Self-matching Networks
- » ReasoNet - Learning to Stop Reading in Machine Comprehension
- » Principled Detection of Out-of-Distribution Examples in Neural Networks
- » One Model To Learn Them All
- » Making the V in VQA Matter - Elevating the Role of Image Understanding in Visual Question Answering
- » Conditional Similarity Networks

- » How to train your MAML
- » Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- » Assessing Generalization in Deep Reinforcement Learning
- » Quantifying Generalization in Reinforcement Learning
- » Set Transformer - A Framework for Attention-based Permutation-Invariant Neural Networks
- » Measuring abstract reasoning in neural networks
- » Meta-Reinforcement Learning of Structured Exploration Strategies
- » Relational Reinforcement Learning
- » Towards a natural benchmark for continual learning
- » Meta-Learning Update Rules for Unsupervised Representation Learning
- » Modular meta-learning
- » Pre-training Graph Neural Networks with Kernels
- » Smooth Loss Functions for Deep Top-k Classification
- » Representation Tradeoffs for Hyperbolic Embeddings
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop
- » When Recurrent Models Don’t Need To Be Recurrent
- » Emergence of Grounded Compositional Language in Multi-Agent Populations
- » A Semantic Loss Function for Deep Learning with Symbolic Knowledge
- » Hierarchical Graph Representation Learning with Differentiable Pooling
- » Kronecker Recurrent Units
- » Learning Independent Causal Mechanisms
- » Memory-based Parameter Adaptation
- » Born Again Neural Networks
- » Learning to Count Objects in Natural Images for Visual Question Answering
- » The Lottery Ticket Hypothesis - Training Pruned Neural Networks
- » Learning an SAT Solver from Single-Bit Supervision
- » Neural Relational Inference for Interacting Systems

- » Gossip based Actor-Learner Architectures for Deep RL
- » PHYRE - A New Benchmark for Physical Reasoning
- » Large Memory Layers with Product Keys
- » Abductive Commonsense Reasoning
- » Hamiltonian Neural Networks
- » Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
- » Good-Enough Compositional Data Augmentation
- » GNN Explainer - A Tool for Post-hoc Explanation of Graph Neural Networks
- » To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
- » Model Primitive Hierarchical Lifelong Reinforcement Learning
- » TuckER - Tensor Factorization for Knowledge Graph Completion
- » Diversity is All You Need - Learning Skills without a Reward Function
- » Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
- » Efficient Lifelong Learning with A-GEM

- » Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
- » Get To The Point - Summarization with Pointer-Generator Networks
- » Reading Wikipedia to Answer Open-Domain Questions
- » Two/Too Simple Adaptations of Word2Vec for Syntax Problems

- » Gossip based Actor-Learner Architectures for Deep RL
- » How to train your MAML
- » PHYRE - A New Benchmark for Physical Reasoning
- » Large Memory Layers with Product Keys
- » Abductive Commonsense Reasoning
- » Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- » Assessing Generalization in Deep Reinforcement Learning
- » Quantifying Generalization in Reinforcement Learning
- » Set Transformer - A Framework for Attention-based Permutation-Invariant Neural Networks
- » Measuring abstract reasoning in neural networks
- » Hamiltonian Neural Networks
- » Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
- » Meta-Reinforcement Learning of Structured Exploration Strategies
- » Relational Reinforcement Learning
- » Good-Enough Compositional Data Augmentation
- » Multiple Model-Based Reinforcement Learning
- » Towards a natural benchmark for continual learning
- » Meta-Learning Update Rules for Unsupervised Representation Learning
- » GNN Explainer - A Tool for Post-hoc Explanation of Graph Neural Networks
- » To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
- » Model Primitive Hierarchical Lifelong Reinforcement Learning
- » TuckER - Tensor Factorization for Knowledge Graph Completion
- » Linguistic Knowledge as Memory for Recurrent Neural Networks
- » Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
- » Efficient Lifelong Learning with A-GEM
- » Pre-training Graph Neural Networks with Kernels
- » Smooth Loss Functions for Deep Top-k Classification
- » Hindsight Experience Replay
- » Representation Tradeoffs for Hyperbolic Embeddings
- » Learned Optimizers that Scale and Generalize
- » One-shot Learning with Memory-Augmented Neural Networks
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop
- » Poincaré Embeddings for Learning Hierarchical Representations
- » When Recurrent Models Don’t Need To Be Recurrent
- » HoME - a Household Multimodal Environment
- » Emergence of Grounded Compositional Language in Multi-Agent Populations
- » A Semantic Loss Function for Deep Learning with Symbolic Knowledge
- » Hierarchical Graph Representation Learning with Differentiable Pooling
- » Imagination-Augmented Agents for Deep Reinforcement Learning
- » Kronecker Recurrent Units
- » Learning Independent Causal Mechanisms
- » Memory-based Parameter Adaptation
- » Born Again Neural Networks
- » Net2Net-Accelerating Learning via Knowledge Transfer
- » Learning to Count Objects in Natural Images for Visual Question Answering
- » Neural Message Passing for Quantum Chemistry
- » Unsupervised Learning by Predicting Noise
- » The Lottery Ticket Hypothesis - Training Pruned Neural Networks
- » Cyclical Learning Rates for Training Neural Networks
- » Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
- » An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
- » Learning an SAT Solver from Single-Bit Supervision
- » Neural Relational Inference for Interacting Systems
- » Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
- » Get To The Point - Summarization with Pointer-Generator Networks
- » Emotional Chatting Machine - Emotional Conversation Generation with Internal and External Memory
- » Exploring Models and Data for Image Question Answering
- » How transferable are features in deep neural networks
- » Distilling the Knowledge in a Neural Network
- » Revisiting Semi-Supervised Learning with Graph Embeddings
- » Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
- » Word Representations via Gaussian Embedding
- » HARP - Hierarchical Representation Learning for Networks
- » Swish - a Self-Gated Activation Function
- » Reading Wikipedia to Answer Open-Domain Questions
- » Task-Oriented Query Reformulation with Reinforcement Learning
- » Refining Source Representations with Relation Networks for Neural Machine Translation
- » Pointer Networks
- » Learning to Compute Word Embeddings On the Fly
- » R-NET - Machine Reading Comprehension with Self-matching Networks
- » ReasoNet - Learning to Stop Reading in Machine Comprehension
- » Principled Detection of Out-of-Distribution Examples in Neural Networks
- » Ask Me Anything - Dynamic Memory Networks for Natural Language Processing
- » One Model To Learn Them All
- » Two/Too Simple Adaptations of Word2Vec for Syntax Problems
- » A Decomposable Attention Model for Natural Language Inference
- » Neural Module Networks
- » Making the V in VQA Matter - Elevating the Role of Image Understanding in Visual Question Answering
- » Conditional Similarity Networks
- » Simple Baseline for Visual Question Answering
- » VQA-Visual Question Answering

- » Large Memory Layers with Product Keys
- » Ask Me Anything - Dynamic Memory Networks for Natural Language Processing
- » One Model To Learn Them All
- » A Decomposable Attention Model for Natural Language Inference

- » Towards a natural benchmark for continual learning
- » Model Primitive Hierarchical Lifelong Reinforcement Learning
- » Efficient Lifelong Learning with A-GEM

- » Efficient Lifelong Learning with A-GEM
- » Net2Net-Accelerating Learning via Knowledge Transfer
- » Learning to Count Objects in Natural Images for Visual Question Answering
- » Unsupervised Learning by Predicting Noise
- » Exploring Models and Data for Image Question Answering
- » How transferable are features in deep neural networks
- » Principled Detection of Out-of-Distribution Examples in Neural Networks
- » One Model To Learn Them All
- » Neural Module Networks
- » Making the V in VQA Matter - Elevating the Role of Image Understanding in Visual Question Answering
- » Conditional Similarity Networks
- » Simple Baseline for Visual Question Answering
- » VQA-Visual Question Answering

- » Towards a natural benchmark for continual learning
- » Model Primitive Hierarchical Lifelong Reinforcement Learning
- » Efficient Lifelong Learning with A-GEM
- » An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

- » Towards a natural benchmark for continual learning
- » Model Primitive Hierarchical Lifelong Reinforcement Learning
- » Efficient Lifelong Learning with A-GEM
- » Memory-based Parameter Adaptation

- » Gossip based Actor-Learner Architectures for Deep RL
- » Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- » Assessing Generalization in Deep Reinforcement Learning
- » Quantifying Generalization in Reinforcement Learning

- » PHYRE - A New Benchmark for Physical Reasoning
- » Abductive Commonsense Reasoning
- » Exploring Models and Data for Image Question Answering
- » Reading Wikipedia to Answer Open-Domain Questions
- » Neural Module Networks
- » VQA-Visual Question Answering

- » Gossip based Actor-Learner Architectures for Deep RL
- » Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- » Assessing Generalization in Deep Reinforcement Learning
- » Quantifying Generalization in Reinforcement Learning
- » Relational Reinforcement Learning

- » Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
- » Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
- » Task-Oriented Query Reformulation with Reinforcement Learning
- » A Decomposable Attention Model for Natural Language Inference
- » A Fast and Accurate Dependency Parser using Neural Networks

- » Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
- » A Decomposable Attention Model for Natural Language Inference

- » Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
- » Task-Oriented Query Reformulation with Reinforcement Learning

- » GNN Explainer - A Tool for Post-hoc Explanation of Graph Neural Networks
- » TuckER - Tensor Factorization for Knowledge Graph Completion
- » Representation Tradeoffs for Hyperbolic Embeddings
- » Poincaré Embeddings for Learning Hierarchical Representations
- » Unsupervised Learning by Predicting Noise
- » StarSpace - Embed All The Things!
- » PTE - Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- » Revisiting Semi-Supervised Learning with Graph Embeddings
- » HARP - Hierarchical Representation Learning for Networks
- » Learning to Compute Word Embeddings On the Fly
- » Two/Too Simple Adaptations of Word2Vec for Syntax Problems
- » Conditional Similarity Networks

- » How to train your MAML
- » To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks

- » Quantifying Generalization in Reinforcement Learning
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop

- » Assessing Generalization in Deep Reinforcement Learning
- » Quantifying Generalization in Reinforcement Learning

- » GNN Explainer - A Tool for Post-hoc Explanation of Graph Neural Networks
- » Pre-training Graph Neural Networks with Kernels
- » Hierarchical Graph Representation Learning with Differentiable Pooling
- » Learning an SAT Solver from Single-Bit Supervision
- » Neural Relational Inference for Interacting Systems

- » Assessing Generalization in Deep Reinforcement Learning
- » Quantifying Generalization in Reinforcement Learning

- » GNN Explainer - A Tool for Post-hoc Explanation of Graph Neural Networks
- » TuckER - Tensor Factorization for Knowledge Graph Completion
- » Pre-training Graph Neural Networks with Kernels
- » Representation Tradeoffs for Hyperbolic Embeddings
- » Poincaré Embeddings for Learning Hierarchical Representations
- » Hierarchical Graph Representation Learning with Differentiable Pooling
- » Neural Message Passing for Quantum Chemistry
- » Learning an SAT Solver from Single-Bit Supervision
- » Neural Relational Inference for Interacting Systems
- » StarSpace - Embed All The Things!
- » PTE - Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- » Revisiting Semi-Supervised Learning with Graph Embeddings
- » Higher-order organization of complex networks
- » Network Motifs - Simple Building Blocks of Complex Networks
- » HARP - Hierarchical Representation Learning for Networks

- » GNN Explainer - A Tool for Post-hoc Explanation of Graph Neural Networks
- » TuckER - Tensor Factorization for Knowledge Graph Completion
- » Pre-training Graph Neural Networks with Kernels
- » Representation Tradeoffs for Hyperbolic Embeddings
- » Poincaré Embeddings for Learning Hierarchical Representations
- » Hierarchical Graph Representation Learning with Differentiable Pooling
- » Neural Message Passing for Quantum Chemistry
- » Neural Relational Inference for Interacting Systems
- » StarSpace - Embed All The Things!
- » Revisiting Semi-Supervised Learning with Graph Embeddings
- » HARP - Hierarchical Representation Learning for Networks

- » Representation Tradeoffs for Hyperbolic Embeddings
- » Poincaré Embeddings for Learning Hierarchical Representations

- » How to train your MAML
- » Measuring abstract reasoning in neural networks
- » Relational Reinforcement Learning
- » Meta-Learning Update Rules for Unsupervised Representation Learning
- » Diversity is All You Need - Learning Skills without a Reward Function
- » Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
- » Efficient Lifelong Learning with A-GEM
- » Smooth Loss Functions for Deep Top-k Classification
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop
- » Memory-based Parameter Adaptation
- » Learning to Count Objects in Natural Images for Visual Question Answering
- » An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
- » Word Representations via Gaussian Embedding

- » Smooth Loss Functions for Deep Top-k Classification
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop
- » Memory-based Parameter Adaptation

- » Measuring abstract reasoning in neural networks
- » Relational Reinforcement Learning
- » Meta-Learning Update Rules for Unsupervised Representation Learning
- » Diversity is All You Need - Learning Skills without a Reward Function
- » Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
- » Efficient Lifelong Learning with A-GEM

- » Quantifying Generalization in Reinforcement Learning
- » Set Transformer - A Framework for Attention-based Permutation-Invariant Neural Networks
- » Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
- » Learned Optimizers that Scale and Generalize
- » A Semantic Loss Function for Deep Learning with Symbolic Knowledge
- » Kronecker Recurrent Units
- » Learning Independent Causal Mechanisms
- » Born Again Neural Networks
- » Revisiting Semi-Supervised Learning with Graph Embeddings

- » Representation Tradeoffs for Hyperbolic Embeddings
- » A Semantic Loss Function for Deep Learning with Symbolic Knowledge
- » Kronecker Recurrent Units
- » Learning Independent Causal Mechanisms
- » Born Again Neural Networks

- » Quantifying Generalization in Reinforcement Learning
- » Set Transformer - A Framework for Attention-based Permutation-Invariant Neural Networks

- » Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
- » Task-Oriented Query Reformulation with Reinforcement Learning

- » PTE - Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- » ReasoNet - Learning to Stop Reading in Machine Comprehension

- » Towards a natural benchmark for continual learning
- » Model Primitive Hierarchical Lifelong Reinforcement Learning
- » Efficient Lifelong Learning with A-GEM
- » Net2Net-Accelerating Learning via Knowledge Transfer

- » Smooth Loss Functions for Deep Top-k Classification
- » A Semantic Loss Function for Deep Learning with Symbolic Knowledge

- » Smooth Loss Functions for Deep Top-k Classification
- » A Semantic Loss Function for Deep Learning with Symbolic Knowledge

- » Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
- » Reading Wikipedia to Answer Open-Domain Questions
- » R-NET - Machine Reading Comprehension with Self-matching Networks
- » ReasoNet - Learning to Stop Reading in Machine Comprehension

- » Large Memory Layers with Product Keys
- » Linguistic Knowledge as Memory for Recurrent Neural Networks
- » One-shot Learning with Memory-Augmented Neural Networks

- » How to train your MAML
- » Meta-Reinforcement Learning of Structured Exploration Strategies
- » Meta-Learning Update Rules for Unsupervised Representation Learning
- » Modular meta-learning
- » Learned Optimizers that Scale and Generalize

- » Multiple Model-Based Reinforcement Learning
- » Imagination-Augmented Agents for Deep Reinforcement Learning

- » Higher-order organization of complex networks
- » Network Motifs - Simple Building Blocks of Complex Networks

- » To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
- » StarSpace - Embed All The Things!

- » Hindsight Experience Replay
- » HoME - a Household Multimodal Environment
- » Imagination-Augmented Agents for Deep Reinforcement Learning
- » Exploring Models and Data for Image Question Answering
- » How transferable are features in deep neural networks
- » Distilling the Knowledge in a Neural Network
- » Pointer Networks

- » How transferable are features in deep neural networks
- » Distilling the Knowledge in a Neural Network

- » Hindsight Experience Replay
- » HoME - a Household Multimodal Environment
- » Imagination-Augmented Agents for Deep Reinforcement Learning

- » Large Memory Layers with Product Keys
- » Abductive Commonsense Reasoning
- » Good-Enough Compositional Data Augmentation
- » To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
- » Linguistic Knowledge as Memory for Recurrent Neural Networks
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop
- » Poincaré Embeddings for Learning Hierarchical Representations
- » When Recurrent Models Don’t Need To Be Recurrent
- » Emergence of Grounded Compositional Language in Multi-Agent Populations
- » Kronecker Recurrent Units
- » Learning to Count Objects in Natural Images for Visual Question Answering
- » Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
- » Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
- » Get To The Point - Summarization with Pointer-Generator Networks
- » StarSpace - Embed All The Things!
- » Emotional Chatting Machine - Emotional Conversation Generation with Internal and External Memory
- » Exploring Models and Data for Image Question Answering
- » PTE - Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- » Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
- » Word Representations via Gaussian Embedding
- » Reading Wikipedia to Answer Open-Domain Questions
- » Task-Oriented Query Reformulation with Reinforcement Learning
- » Refining Source Representations with Relation Networks for Neural Machine Translation
- » Pointer Networks
- » Learning to Compute Word Embeddings On the Fly
- » R-NET - Machine Reading Comprehension with Self-matching Networks
- » ReasoNet - Learning to Stop Reading in Machine Comprehension
- » Ask Me Anything - Dynamic Memory Networks for Natural Language Processing
- » One Model To Learn Them All
- » Two/Too Simple Adaptations of Word2Vec for Syntax Problems
- » A Decomposable Attention Model for Natural Language Inference
- » A Fast and Accurate Dependency Parser using Neural Networks
- » Neural Module Networks
- » Simple Baseline for Visual Question Answering
- » VQA-Visual Question Answering

- » Large Memory Layers with Product Keys
- » Abductive Commonsense Reasoning
- » To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
- » Linguistic Knowledge as Memory for Recurrent Neural Networks
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop
- » Poincaré Embeddings for Learning Hierarchical Representations
- » When Recurrent Models Don’t Need To Be Recurrent
- » Emergence of Grounded Compositional Language in Multi-Agent Populations

- » PTE - Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- » Higher-order organization of complex networks
- » Network Motifs - Simple Building Blocks of Complex Networks

- » Meta-Reinforcement Learning of Structured Exploration Strategies
- » Towards a natural benchmark for continual learning

- » Gossip based Actor-Learner Architectures for Deep RL
- » Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

- » Representation Tradeoffs for Hyperbolic Embeddings
- » Poincaré Embeddings for Learning Hierarchical Representations

- » To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
- » Linguistic Knowledge as Memory for Recurrent Neural Networks
- » Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
- » Reading Wikipedia to Answer Open-Domain Questions
- » R-NET - Machine Reading Comprehension with Self-matching Networks
- » ReasoNet - Learning to Stop Reading in Machine Comprehension
- » Ask Me Anything - Dynamic Memory Networks for Natural Language Processing

- » Gossip based Actor-Learner Architectures for Deep RL
- » Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- » Assessing Generalization in Deep Reinforcement Learning
- » Quantifying Generalization in Reinforcement Learning
- » Meta-Reinforcement Learning of Structured Exploration Strategies
- » Relational Reinforcement Learning
- » Multiple Model-Based Reinforcement Learning
- » Model Primitive Hierarchical Lifelong Reinforcement Learning
- » Diversity is All You Need - Learning Skills without a Reward Function
- » Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
- » Hindsight Experience Replay
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop
- » Imagination-Augmented Agents for Deep Reinforcement Learning
- » Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
- » Task-Oriented Query Reformulation with Reinforcement Learning
- » R-NET - Machine Reading Comprehension with Self-matching Networks
- » ReasoNet - Learning to Stop Reading in Machine Comprehension

- » Linguistic Knowledge as Memory for Recurrent Neural Networks
- » Learned Optimizers that Scale and Generalize
- » When Recurrent Models Don’t Need To Be Recurrent
- » Kronecker Recurrent Units

- » Gossip based Actor-Learner Architectures for Deep RL
- » Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- » Assessing Generalization in Deep Reinforcement Learning
- » Meta-Reinforcement Learning of Structured Exploration Strategies
- » Relational Reinforcement Learning
- » Multiple Model-Based Reinforcement Learning
- » Model Primitive Hierarchical Lifelong Reinforcement Learning
- » Diversity is All You Need - Learning Skills without a Reward Function
- » Hindsight Experience Replay
- » BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop

- » Set Transformer - A Framework for Attention-based Permutation-Invariant Neural Networks
- » Measuring abstract reasoning in neural networks

- » Set Transformer - A Framework for Attention-based Permutation-Invariant Neural Networks
- » Measuring abstract reasoning in neural networks
- » Relational Reinforcement Learning

- » StarSpace - Embed All The Things!
- » Word Representations via Gaussian Embedding
- » Refining Source Representations with Relation Networks for Neural Machine Translation

- » Learning to Count Objects in Natural Images for Visual Question Answering
- » Get To The Point - Summarization with Pointer-Generator Networks
- » HARP - Hierarchical Representation Learning for Networks
- » Swish - a Self-Gated Activation Function
- » R-NET - Machine Reading Comprehension with Self-matching Networks
- » ReasoNet - Learning to Stop Reading in Machine Comprehension
- » Ask Me Anything - Dynamic Memory Networks for Natural Language Processing
- » A Decomposable Attention Model for Natural Language Inference

- » Higher-order organization of complex networks
- » Network Motifs - Simple Building Blocks of Complex Networks

- » To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
- » How transferable are features in deep neural networks
- » Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

- » Meta-Learning Update Rules for Unsupervised Representation Learning
- » Diversity is All You Need - Learning Skills without a Reward Function
- » Unsupervised Learning by Predicting Noise

- » Learning to Count Objects in Natural Images for Visual Question Answering
- » Exploring Models and Data for Image Question Answering
- » Neural Module Networks
- » Simple Baseline for Visual Question Answering
- » VQA-Visual Question Answering

- » StarSpace - Embed All The Things!
- » Word Representations via Gaussian Embedding
- » Two/Too Simple Adaptations of Word2Vec for Syntax Problems