深度之眼Paper带读笔记目录

简介

本次的Paper学习营分CV和NLP两个方向，每个方向又分精读、重点阅读和推荐阅读三类文章，精读基本每篇文章都分三部分：论文导读、论文精读、代码讲解。
为了快速了解各种方法、框架，代码部分我基本就略过，等遇到实际问题再上手-。-
两个方向的第一篇文章是一样的。
所有论文都可以从谷歌学术上找到。

NLP精读论文目录（已完结）

01.Deep learning：Deep learning
02.word2vec：Efficient Estimation of Word Representations in Vector Space
03.句和文档的embedding：Distributed representations of sentences and docments
04.machine translation：Neural Machine Translation by Jointly Learning to Align and Translate
05.transformer：Transformer: attention is all you need
06.GloVe：GloVe: Global Vectors for Word Representation
07.Skip：Skip-Thought Vector
08.TextCNN：Convolutional Neural Networks for Sentence Classification
09.基于CNN的词级别的文本分类：Character-level Convolutional Networks for Text Classification
10.DCNN：A Convolutional Neural Network For Modelling Sentences
11.FASTTEXT：Bag of Tricks for Efficient Text Classification
12.HAN：Hierarchical Attention Network for Document Classification
13.PCNNATT：Neural Relation Extraction with Selective Attention over Instances
14.E2ECRF：End-to-end Sequence Labeling via Bi-directional LSTM-CNNS-CRF
15.多层LSTM：Sequence to Sequence Learning with Neural Networks
16.卷积seq2seq：Convolutional Sequence to Sequence Learning
17.GNMT：Google’s Neural Machine Translation System：Bridging the Gap between Human and Machine Translation
18.UMT：Phrase-Based&Neural Unsupervised Machine Translation
19.指针生成网络：Get To The Point:Summarization with Pointer-Generator Networks
20.End-to-End Memory Networks：End-to-End Memory Networks
21.QANet：QANet:Combining Local Convolution with Global Self-Attention for Reading Comprehension
22.双向Attention：Bi-Directional Attention Flow for Machine Comprehension
23.Dialogue：Adversarial Learning for Neural Dialogue Generation
24.缺
25.R-GCNs：Modeling Relational Data with GraphConvolutional Networks
26.大规模语料模型：Exploring the limits of language model
27.Transformer-XL：Transformer-XL:Attentive Language Models Beyond a Fixed-Length Context
28.TCN：An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
29.Deep contextualized word representations
30.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding

CV目录

这里和之前的CV paper学习营内容有变化，待更新。
01.Deep learning：Deep learning
02.AlexNet：ImageNet Classification with Deep Convolutional Neural Networks