SUMMARY.md

Table of contents

• PaperHighlights
• 2019
  • 03
    • Not All Contexts Are Created Equal Better Word Representations with Variable Attention
    • Learning Context-Sensitive Word Embeddings with Neural Tensor Skip-Gram Model
    • Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
    • pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference
    • Contextual Word Representations: A Contextual Introduction
    • Not All Neural Embeddings are Born Equal
    • High-risk learning: acquiring new word vectors from tiny data
    • Learning word embeddings from dictionary definitions only
    • Dependency-Based Word Embeddings
  • 02
    • Improving Word Embedding Compositionality using Lexicographic Definitions
    • From Word Embeddings To Document Distances
    • Progressive Growing of GANs for Improved Quality, Stability, and Variation
    • Retrofitting Word Vectors to Semantic Lexicons
    • Bag of Tricks for Image Classification with Convolutional Neural Networks
    • Multi-Task Deep Neural Networks for Natural Language Understanding
    • Snapshot Ensembles: Train 1, get M for free
    • EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
    • Counter-fitting Word Vectors to Linguistic Constraints
    • AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
    • Learning semantic similarity in a continuous space
    • Progressive Neural Networks
    • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
    • Language Models are Unsupervised Multitask Learners
  • 01
    • Querying Word Embeddings for Similarity and Relatedness
    • Data Distillation: Towards Omni-Supervised Learning
    • A Rank-Based Similarity Metric for Word Embeddings
    • Dict2vec: Learning Word Embeddings using Lexical Dictionaries
    • Graph Convolutional Networks for Text Classification
    • Improving Distributional Similarity with Lessons Learned from Word Embeddings
    • Real-time Personalization using Embeddings for Search Ranking at Airbnb
    • Glyce: Glyph-vectors for Chinese Character Representations
    • Auto-Encoding Dictionary Definitions into Consistent Word Embeddings
    • Distilling the Knowledge in a Neural Network
    • Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrin
    • The (Too Many) Problems of Analogical Reasoning with Word Vectors
    • Linear Ensembles of Word Embedding Models
    • Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance
    • Dynamic Meta-Embeddings for Improved Sentence Representations
• 2018
  • 11
    • Think Globally, Embed Locally — Locally Linear Meta-embedding of Words
    • Learning linear transformations between counting-based and prediction-based word embeddings
    • Learning Word Meta-Embeddings by Autoencoding
    • Learning Word Meta-Embeddings
    • Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings
  • 6
    • Universal Language Model Fine-tuning for Text Classification
    • Semi-supervised sequence tagging with bidirectional language models
    • Consensus Attention-based Neural Networks for Chinese Reading Comprehension
    • Attention-over-Attention Neural Networks for Reading Comprehension
    • Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
    • Convolutional Neural Networks for Sentence Classification
    • Deep contextualized word representations
    • Neural Architectures for Named Entity Recognition
    • Improving Language Understanding by Generative Pre-Training
    • A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence C
    • Teaching Machines to Read and Comprehend
  • 5
    • Text Understanding with the Attention Sum Reader Network
    • Effective Approaches to Attention-based Neural Machine Translation
    • Distance-based Self-Attention Network for Natural Language Inference
    • Deep Residual Learning for Image Recognition
    • U-Net: Convolutional Networks for Biomedical Image Segmentation
    • Memory Networks
    • Neural Machine Translation by Jointly Learning to Align and Translate
    • Convolutional Sequence to Sequence Learning
    • An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
    • Graph Attention Networks
    • Attention is All You Need
    • DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
    • A Structured Self-attentive Sentence Embedding
    • Hierarchical Attention Networks for Document Classification
    • Grammar as a Foreign Language
    • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
    • Transforming Auto-encoders
    • Self-Attention with Relative Position Representations
  • 1
    • 20180108-20180114
• 2017
  • 12
    • 20171218-2017124 论文笔记
  • 11
    • 20171127-20171203 论文笔记 1
    • 20171106-20171126 论文笔记
    • 20171030-20171105 论文笔记 1
• Paper Title as Note Title