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I read papers, and here are my highlights.
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2019
Last updated
5 years ago
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2018-11
2018-06
2018-05
历史遗留笔记 (周记)
Learning Word Meta-Embeddings
Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings
Teaching Machines to Read and Comprehend
Attention-over-Attention Neural Networks for Reading Comprehension
Consensus Attention-based Neural Networks for Chinese Reading Comprehension
Convolutional Neural Networks for Sentence Classification
Deep contextualized word representations
A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification
Attention Is All You Need
Neural Machine Translation by Jointly Learning to Align and Translate
U-Net: Convolutional Networks for Biomedical Image Segmentation
Transforming Auto-encoders
Text Understanding with the Attention Sum Reader Network
Teaching Machines to Read and Comprehend
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Self-Attention with Relative Position Representations
Deep Residual Learning for Image Recognition
Memory Networks
Hierarchical Attention Networks for Document Classification
Graph Attention Networks
Grammar as a Foreign Language
Effective Approaches to Attention-based Neural Machine Translation
Distance-based Self-Attention Network for Natural Language Inference
Convolutional Sequence to Sequence Learning
A Structured Self-attentive Sentence Embedding
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
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