深度之眼Paper带读笔记目录

Posted 2022-06-22 oldmao_2000

文章目录

• 简介
• 图神经网络（已完结）
• NLP精读论文目录（已完结）
• NLP Baseline（已完结）
• CV目录（已太监）

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。
2020.7.15 更新说明，在原来NLP精读论文的基础上，增加NLP Baseline学习，内容更加详细，多了代码讲解。可以看有Baseline标注的即可。

图神经网络（已完结）

01.Node2Vec：Node2Vec: Scalable Feature Learning for Networks
02.LINE：LINE: Large-scale Information Network Embedding
03.SDNE：Structural Deep Network Embedding
04.metapath2vec：metapath2vec:Scalable Representation Learning for Heterogeneous Networks
05.TransE/H/R/D:
TransE：Translating Embeddings for Modeling Multi-relational Data
TransH：Knowledge Graph Embedding by Translating on Hyperplanes
TransR：Learning entity and relation embeddings for knowledge graph completion
TransD：Knowledge Graph Embedding via Dynamic Mapping Matrix
06.GAT：Graph Attention Networks
07.GraphSAGE：Inductive Representation kearping on Large Graphs
08.GCN：Semi-Supervised Classification with Graph Convolutional Networks
09.GGNN：Gated Graph Sequence Neural Networks
10.MPNN：Neural Message Passing for Quantum Chemistry

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

NLP Baseline（已完结）

1.Word2Vec.Efficient Estimation of Word Representations in Vector Space
2.GloVe.GloVe: Global Vectors for Word Representation
3.C2W.Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
4.TextCNN.Convolutional Neural Networks for Sentence Classification
5.CharCNN.Character-level Convolutional Networks for Text Classification
6.FastText.Bag of Tricks for Efficient Text Classification
7.Seq2Seq.Sequence to Sequence Learning with Neural Networks
8.Attention NMT.Neural Machine Translation by Jointly Learning to Align and Translate
9.HAN.Hierarchical Attention Network for Document Classification
10.SGM.SGM: Sequence Generation Model for Multi-Label Classification

CV目录（已太监）

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