README.md

papers-I-read

I am trying a new initiative - a-paper-a-week. This repository will hold all those papers and related summaries and notes.

List of papers

• 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
• Diversity is All You Need - Learning Skills without a Reward Function
• Modular meta-learning
• Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
• Efficient Lifelong Learningi 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
• StarSpace - Embed All The Things!
• 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
• Higher-order organization of complex networks
• Network Motifs - Simple Building Blocks of Complex Networks
• 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
• A Fast and Accurate Dependency Parser using Neural Networks
• 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
• Learning to Generate Reviews and Discovering Sentiment
• Seeing the Arrow of Time
• End-to-end optimization of goal-driven and visually grounded dialogue systems
• GuessWhat?! Visual object discovery through multi-modal dialogue
• Semantic Parsing via Paraphrasing
• Traversing Knowledge Graphs in Vector Space
• PPDB: The Paraphrase Database
• NewsQA: A Machine Comprehension Dataset
• A Persona-Based Neural Conversation Model
• “Why Should I Trust You?” Explaining the Predictions of Any Classifier
• Conditional Generative Adversarial Nets
• Addressing the Rare Word Problem in Neural Machine Translation
• Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
• Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
• Improving Word Representations via Global Context and Multiple Word Prototypes
• Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
• Skip-Thought Vectors
• Deep Convolutional Generative Adversarial Nets
• Generative Adversarial Nets
• A Roadmap towards Machine Intelligence
• Smart Reply: Automated Response Suggestion for Email
• Convolutional Neural Network For Sentence Classification
• Conditional Image Generation with PixelCNN Decoders
• Pixel Recurrent Neural Networks
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• Bag of Tricks for Efficient Text Classification
• GloVe: Global Vectors for Word Representation
• SimRank: A Measure of Structural-Context Similarity
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
• WikiReading : A Novel Large-scale Language Understanding Task over Wikipedia
• WikiQA: A challenge dataset for open-domain question answering
• Teaching Machines to Read and Comprehend
• Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems
• Recurrent Neural Network Regularization
• Deep Math: Deep Sequence Models for Premise Selection
• A Neural Conversational Model
• Key-Value Memory Networks for Directly Reading Documents
• Advances In Optimizing Recurrent Networks
• Query Regression Networks for Machine Comprehension
• Sequence to Sequence Learning with Neural Networks
• The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training
• Question Answering with Subgraph Embeddings
• Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
• Visualizing Large-scale and High-dimensional Data
• Visualizing Data using t-SNE
• Curriculum Learning
• End-To-End Memory Networks
• Memory Networks
• Learning To Execute
• Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud
• Large Scale Distributed Deep Networks
• Efficient Estimation of Word Representations in Vector Space
• Regularization and variable selection via the elastic net
• Fractional Max-Pooling
• TAO: Facebook’s Distributed Data Store for the Social Graph
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• The Unified Logging Infrastructure for Data Analytics at Twitter
• A Few Useful Things to Know about Machine Learning
• Hive – A Petabyte Scale Data Warehouse Using Hadoop
• Kafka: a Distributed Messaging System for Log Processing
• Power-law distributions in Empirical data
• Pregel: A System for Large-Scale Graph Processing
• GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
• Pig Latin: A Not-So-Foreign Language for Data Processing
• Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing
• MapReduce: Simplified Data Processing on Large Clusters
• BigTable: A Distributed Storage System for Structured Data
• Spark SQL: Relational Data Processing in Spark
• Spark: Cluster Computing with Working Sets
• Fast Data in the Era of Big Data: Twitter’s Real-Time Related Query Suggestion Architecture
• Scaling Memcache at Facebook
• Dynamo: Amazon’s Highly Available Key-value Store
• f4 : Facebook's Warm BLOB Storage System
• A Theoretician’s Guide to the Experimental Analysis of Algorithms
• Cuckoo Hashing
• Never Ending Learning