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https://github.com/tjunlp-lab/awesome-NLP-Machine-Learning

A list of paper & code on machine learning techniques for NLP research, including RL/Self-supervised Learning/VAE/GAN/Meta learning
https://github.com/tjunlp-lab/awesome-NLP-Machine-Learning
List: awesome-NLP-Machine-Learning
Last synced: 3 months ago
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A list of paper & code on machine learning techniques for NLP research, including RL/Self-supervised Learning/VAE/GAN/Meta learning
Host: GitHub
URL: https://github.com/tjunlp-lab/awesome-NLP-Machine-Learning
Owner: tjunlp-lab
Created: 2020-03-06T14:41:40.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2020-03-13T13:48:45.000Z (over 4 years ago)
Last Synced: 2024-04-11T08:03:05.526Z (7 months ago)
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Stars: 34
Watchers: 4
Forks: 12
Open Issues: 0
Metadata Files:
- Readme: Readme.md
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ultimate-awesome - awesome-NLP-Machine-Learning - A list of paper & code on machine learning techniques for NLP research, including RL/Self-supervised Learning/VAE/GAN/Meta learning. (Other Lists / PowerShell Lists)
README

        
## Table of Contents

- [Reinforcement Learning](#Reinforcement-Learning)

  - [Dialogue](#RL-Dialogue)

  - [Survey](#RL-Survey)

  - [Summarization](#RL-Summarization)

- [Self-Supervised Learning](#self-supervised-learning)

  - [Word Embedding](#SSL-Word-Embedding)

  - [Dialogue](#SSL-Dialogue)

  - [Vision & Language](#SSL-Vision&Language)

  - [Machine Translation](#SSL-Machine-Translation)

  - [Named Entity Recognition](#SSL-Named-Entity-Recognition)

  - [Parsing](#SSL-Parsing)

  - [Qestion Answering](#SSL-Qestion-Answering)

  - [Sentiment Analysis](#SSL-Sentiment-Analysis)

  - [Speech Translation](#SSL-Speech-Translation)

  - [Automatic Speech Recognition](#SSL-Automatic-Speech-Recognition)

  - [Summarization](#SSL-Summarization)

  - [Tagging](#SSL-Tagging)

  - [Others](#SSL-Others)

- [VAE](#VAE)

  - [Theory](#VAE-Theory)

  - [Summarization](#VAE-Summarization)

  - [Dialogue](#VAE-Dialogue)

  - [Machine Translation](#VAE-Machine-Translation)

  - [Others](#VAE-Others)

- [GAN](#GAN)

  - [Theory](#GAN-Theory)

  - [Training Skill](#GAN-Training-Skill)

  - [Modeling](#GAN-Modeling)

  - [Sequence GAN](#GAN-Sequencce-GAN)

  - [Machine translation](#GAN-Machine-translation)

  - [Generation](#GAN-Generation)

  - [Classification](#GAN-Classification)

  - [Dialogue](#GAN-Dialogue)

  - [Other Applications](#GAN-Applications)

- [Meta Learning](#Meta-Learning)

  - [Genaration](#ML-Genaration)

  - [Classification](#ML-Classification)

  - [Machine Translation](#ML-Machine-Translation)

  - [Auto Machine Learning](#ML-Auto-Meachine-Learning)

  - [Vision&Language](#ML-Vision&Language)

  - [Question Answering](#ML-Question-Answering)

  - [Others](#ML-Others)

- [Lifelong Learning/Continual Learning](#Lifelong-Learning)

  - [Survey](#LLL-Survey)

  - [Theory](#LLL-Theory)

  - [Approaches](#LLL-Approaches)

- [Graph Neural Networks](#Graph-Neural-Networks)

  - [Survey](#GNN-Survey)

  - [Types](#GNN-Types)

  - [Natural Language Processing](#GNN-NLP)

  - [Computer Vision](#GNN-CV)

# Reinforcement-Learning

## RL-Dialogue

- AAAI 2020-Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation[[pdf]](https://arxiv.org/pdf/2003.00639.pdf)[[code]](https://github.com/hengyicai/Adaptive_Multi-curricula_Learning_for_Dialog)

- AAAI 2020-AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses[[pdf]](https://arxiv.org/pdf/2001.05467.pdf)

- AAAI 2020-Generating Persona Consistent Dialogues by Exploiting Natural Language Inference[[pdf]](https://arxiv.org/pdf/1911.05889.pdf)

- NAACL 2019-Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models[[pdf]](https://arxiv.org/pdf/1902.08858.pdf)[[code]](https://github.com/snakeztc/NeuralDialog-LaRL)

- NAACL 2019-Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat[[pdf]](https://arxiv.org/pdf/1809.03408.pdf)[[code]](https://github.com/shekharRavi/Beyond-Task-Success-NAACL2019)

- ICMLA 2019-Natural Language Generation Using Reinforcement Learning with External Rewards[[pdf]](https://arxiv.org/pdf/1911.11404.pdf)

- IJCNN 2019-Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards[[pdf]](https://arxiv.org/pdf/1908.10331.pdf)

- ICLR 2019-A Study of State Aliasing in Structured Prediction with RNNs[[pdf]](https://arxiv.org/pdf/1906.09310.pdf)

- ACL 2019-Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment[[pdf]](https://arxiv.org/pdf/1906.00549.pdf)

- TASLP-AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning[[pdf]](https://arxiv.org/pdf/1905.11259.pdf)

- AAAI 2019-Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning[[pdf]](https://arxiv.org/pdf/1812.03509.pdf)

- AAAI 2019-Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning[[pdf]](https://arxiv.org/pdf/1811.07550.pdf)[[code]](https://github.com/CrickWu/Swtich-DDQ)

- AAAI 2019-Goal-oriented Dialogue Policy Learning from Failures[[pdf]](https://arxiv.org/pdf/1808.06497.pdf)

- NAACL 2018-Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems[[pdf]](https://arxiv.org/pdf/1804.06512v1.pdf)

- TASLP-Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces[[pdf]](https://arxiv.org/pdf/1802.03753.pdf)

- EMNLP 2018-Subgoal Discovery for Hierarchical Dialogue Policy Learning[[pdf]](https://arxiv.org/pdf/1804.07855.pdf)[[code]](none)

- NAACL 2018-Feudal Reinforcement Learning for Dialogue Management in Large Domains[[pdf]](https://arxiv.org/pdf/1803.03232.pdf)

- ICLR 2018-Towards Explainable and Controllable Open Domain Dialogue Generation with Dialogue Acts[[pdf]](https://arxiv.org/pdf/1807.07255.pdf)

- ACL 2018-Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning[[pdf]](https://arxiv.org/pdf/1801.06176.pdf)[[code]](https://github.com/MiuLab/DDQ)

- NIPS 2017-End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning[[pdf]](https://arxiv.org/pdf/1711.10712.pdf)

- AAAI 2018-BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems[[pdf]](https://arxiv.org/abs/1711.05715)

- SigDial 2017-Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management[[pdf]](https://arxiv.org/pdf/1707.00130.pdf)

- EMNLP 2017-Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning[[pdf]](https://arxiv.org/pdf/1704.03084.pdf)

- IJCNLP 2017-End-to-End Task-Completion Neural Dialogue Systems[[pdf]](https://arxiv.org/pdf/1703.01008.pdf)[[code]](https://github.com/MiuLab/TC-Bot)

- ACL 2017-Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access[[pdf]](https://arxiv.org/pdf/1609.00777.pdf)[[code]](https://github.com/MiuLab/KB-InfoBot)

- arxiv-Deep Reinforcement Learning for Dialogue Generation[[pdf]](https://arxiv.org/pdf/1606.01541.pdf)

## RL-Survey

- IJCAI 2019-A Survey of Reinforcement Learning Informed by Natural Language[[pdf]](https://arxiv.org/pdf/1906.03926.pdf)

- arxiv-A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions[[pdf]](https://arxiv.org/abs/2001.06921)

- arxiv-Deep Reinforcement Learning[[pdf]](https://arxiv.org/pdf/1810.06339.pdf)

- Reinforcement Learning in Natural Language Processing

- Reinforcement Learning for NLP

## RL-Summarization

- ICLR2020-Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach[[pdf]](https://arxiv.org/pdf/1910.03177.pdf)[[code]](https://github.com/rajeev595/RHS_HierNSE)

- EDSMLS 2020-Quality of syntactic implication of RL-based sentence summarization[[pdf]](https://arxiv.org/pdf/1912.05493.pdf)[[code]](https://github.com/lethienhoa/Eval-RL)

- EMNLP 2019-Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization.[[pdf]](https://arxiv.org/pdf/1909.00141.pdf)

- NAACL 2019-Guiding Extractive Summarization with Question-Answering Rewards[[pdf]](https://www.aclweb.org/anthology/N19-1264.pdf)[[code]](https://github.com/ucfnlp/summ_qa_rewards)

- EMNLP 2019-Summary Level Training of Sentence Rewriting for Abstractive Summarization[[pdf]](https://arxiv.org/pdf/1909.08752.pdf)

- EMNLP 2019-Answers Unite! Unsupervised Metrics for Reinforced Summarization Models[[pdf]](https://arxiv.org/pdf/1909.01610.pdf)[[code]](https://github.com/recitalAI/summa-qa)

- EMNLP 2019 -Summary Level Training of Sentence Rewriting for Abstractive Summarization[[pdf]](https://arxiv.org/pdf/1909.08752.pdf)

- EMNLP 2019-An Entity-Driven Framework for Abstractive Summarization[[pdf]](https://arxiv.org/pdf/1909.02059.pdf)[[code]](https://github.com/luyang-huang96/EntityDrivenSumm)

- CoNLL 2019-Pretraining-Based Natural Language Generation for Text Summarization[[pdf]](https://arxiv.org/pdf/1902.09243.pdf)

- ICLR 2018-A Deep Reinforced Model for Abstractive Summarization[[pdf]](https://arxiv.org/pdf/1705.04304.pdf)[[code]](https://github.com/oceanypt/A-DEEP-REINFORCED-MODEL-FOR-ABSTRACTIVE-SUMMARIZATION)

- EMNLP 2018-Improving Abstraction in Text Summarization[[pdf]](https://arxiv.org/pdf/1808.07913.pdf)

- ACL 2018-Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting[[pdf]](https://www.aclweb.org/anthology/P18-1063/)[[code]](https://github.com/ChenRocks/fast_abs_rl)

- NAACL 2018-Multi-Reward Reinforced Summarization with Saliency and Entailment[[pdf]](https://arxiv.org/pdf/1804.06451.pdf)

- EMNLP 2018-Closed-Book Training to Improve Summarization Encoder Memory[[pdf]](https://arxiv.org/pdf/1809.04585.pdf)

- IJCAI-ECAI 2018-A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization[[pdf]](https://arxiv.org/pdf/1805.03616.pdf)

- NAACL 2018-Ranking Sentences for Extractive Summarization with Reinforcement Learning.[[pdf]](https://arxiv.org/pdf/1802.08636.pdf)[[code]](https://github.com/EdinburghNLP/Refresh)

- EMNLP 2018-BANDITSUM: Extractive Summarization as a Contextual Bandit[[pdf]](https://arxiv.org/pdf/1809.09672.pdf)[[code]](https://github.com/yuedongP/BanditSum)

- ACL 2018-Reinforced Extractive Summarization with Question-Focused Rewards[[pdf]](https://arxiv.org/pdf/1805.10392.pdf)

- NAACL 2018-Multi-Reward Reinforced Summarization with Saliency and Entailment[[pdf]](https://arxiv.org/pdf/1804.06451.pdf)

- NAACL 2018-Deep Communicating Agents for Abstractive Summarization[[pdf]](https://arxiv.org/pdf/1803.10357.pdf)[[code]](https://github.com/quentin-burthier/DCA)

- AAAI 2018-Generative Adversarial Network for Abstractive Text Summarization[[pdf]](https://arxiv.org/pdf/1711.09357.pdf)[[code]](https://likicode.com/textsum/)

- IJCNLP 2017-Automatic Text Summarization Using Reinforcement Learning with Embedding Features[[pdf]](https://www.aclweb.org/anthology/I17-2033.pdf)

# Self-Supervised-Learning

## SSL-Word-Embedding

- ECAI 2020-Refinement of Unsupervised Cross-Lingual Word Embeddings[[pdf]](https://arxiv.org/pdf/2002.09213.pdf)

- ICLR 2020-A Mutual Information Maximization Perspective of Language Representation Learning[[pdf]](https://openreview.net/pdf?id=Syx79eBKwr)

- ICLR 2020-albert: a lite bert for self-supervised learning of language representations[[pdf]](https://arxiv.org/pdf/1909.11942.pdf)[[code]](https://github.com/google-research/ALBERT)

- arxiv-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[[pdf]](https://arxiv.org/pdf/1810.04805.pdf?source=post_elevate_sequence_page)[[code]](https://github.com/google-research/bert)

- NAACL 2018-Deep contextualized word representations[[pdf]](https://arxiv.org/pdf/1802.05365.pdf%E3%80%91)

- EMNLP 2018-Semi-Supervised Sequence Modeling with Cross-View Training[[pdf]](https://arxiv.org/pdf/1809.08370.pdf)[[code]](https://github.com/tensorflow/models/tree/master/research/cvt_text)

- EMNLP 2017-Supervised Learning of Universal Sentence Representations from Natural Language Inference Data[[pdf]](https://arxiv.org/pdf/1705.02364.pdf)[[code]](https://www.github.com/facebookresearch/InferSent)

- NAACL 2018-Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings[[pdf]](https://www.aclweb.org/anthology/W18-1706/)

## SSL-Dialogue

- EMNLP 2019-Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue[[pdf]](https://www.aclweb.org/anthology/D19-1203.pdf)[[code]](https://github.com/facebookresearch/ParlAI)

- ACL 2019-Self-Supervised Dialogue Learning[[pdf]](https://arxiv.org/pdf/1907.00448.pdf)

## SSL-Vision&Language

- ACM MM 2019-Unpaired Cross-lingual Image Caption Generation with Self-Supervised Rewards[[pdf]](https://dl.acm.org/citation.cfm?id=3350996)

- ICMR 2019-Self-Supervised Visual Representations for Cross-Modal Retrieval[[pdf]](https://arxiv.org/pdf/1902.00378.pdf)

- arxiv-Towards a Hypothesis on Visual Transformation based Self-Supervision[[pdf]](https://arxiv.org/pdf/1911.10594.pdf)

- arxiv-Learning Video Representations using Contrastive Bidirectional Transformer[[pdf]](https://arxiv.org/pdf/1906.05743.pdf)

## SSL-Machine-Translation

- ACL 2019-Self-Supervised Neural Machine Translation[[pdf]](https://www.aclweb.org/anthology/P19-1178.pdf)

- (book)Joint Training for Neural Machine Translation 2019-Semi-Supervised Learning for Neural Machine Translation[[pdf]](https://arxiv.org/pdf/1606.04596.pdf)

## SSL-Named-Entity-Recognition

- AAAI 2017-A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media[[pdf]](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14484/14201)

## SSL-Parsing

- ACL 2019-Compound Probabilistic Context-Free Grammars for Grammar Induction[[pdf]](https://arxiv.org/abs/1906.10225)[[code]](https://github.com/harvardnlp/compound-pcfg)

- EMNLP 2019-Tree Transformer: Integrating Tree Structures into Self-Attention[[pdf]](https://arxiv.org/abs/1909.06639)[[code]](https://github.com/yaushian/Tree-Transformer)

- NAACL 2019-Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders[[pdf]](https://www.aclweb.org/anthology/N19-1116)[[code]](https://github.com/iesl/diora)

- NAACL 2019-Unsupervised Recurrent Neural Network Grammars[[pdf]](https://www.aclweb.org/anthology/N19-1114)[[code]](https://github.com/harvardnlp/urnng)

- ICLR 2018-Neural Language Modeling by Jointly Learning Syntax and Lexicon[[pdf]](https://openreview.net/forum?id=rkgOLb-0W)[[code]](https://github.com/yikangshen/PRPN)

- ICLR 2019-Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks[[pdf]](https://arxiv.org/abs/1810.09536)[[code]](https://github.com/yikangshen/Ordered-Neurons)

## SSL-Qestion-Answering

- ACL 2019-Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension[[pdf]](https://www.aclweb.org/anthology/P19-1347.pdf)

## SSL-Sentiment-Analysis

- SIGKDD 2017-Large Scale Sentiment Learning with Limited Labels[[pdf]](https://www.researchgate.net/profile/Eirini_Ntoutsi/publication/318920269_Large_Scale_Sentiment_Learning_with_Limited_Labels/links/59f611f30f7e9b553ebd252b/Large-Scale-Sentiment-Learning-with-Limited-Labels.pdf)

- ACL 2019-Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis[[pdf]](https://www.aclweb.org/anthology/P19-1053.pdf)[[code]](https://github.com/DeepLearnXMU/PSSAttention)

## SSL-Speech-Translation

- ICASSP 2020-Generative Pre-Training for Speech with Autoregressive Predictive Coding[[pdf]](https://arxiv.org/pdf/1910.12607.pdf)[[code]](https://github.com/iamyuanchung/Autoregressive-Predictive-Coding)

## SSL-Automatic-Speech-Recognition

- Interspeech 2019-Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text[[pdf]](https://arxiv.org/pdf/1905.01152.pdf)[[code]](https://github.com/espnet/espnet)

## SSL-Summarization

- EMNLP 2019-BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle[[pdf]](https://www.aclweb.org/anthology/D19-1389.pdf)

- ACL 2019-Self-Supervised Learning for Contextualized Extractive Summarization[[pdf]](https://arxiv.org/pdf/1906.04466.pdf)[[code]](https://github.com/hongwang600/Summarization)

## SSL-Tagging

- EMNLP 2019-Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders.[[pdf]](https://www.aclweb.org/anthology/D19-1161/)[[code]](none)

## SSL-Others

- National Science Review 2017-A brief introduction to weakly supervised learning [[pdf]](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/nsr18.pdf)

- Algorithms 2018-An Auto-Adjustable Semi-Supervised Self-Training Algorithm[[pdf]](https://www.mdpi.com/1999-4893/11/9/139/pdf)

- AAAI2017-Learning Safe Prediction for Semi-Supervised Regression[[pdf]](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14587/14396)

- ICLR 2020-Revisiting Self-Training for Neural Sequence Generation[[pdf]](https://openreview.net/pdf?id=SJgdnAVKDH)[[code]](https://github.com/jxhe/self-training-text-generation)

# VAE

## VAE-Theory

- ICLR2013-Auto-Encoding Variational Bayes[[pdf]](https://arxiv.org/pdf/1312.6114.pdf)

- NIPS2015-Learning Structured Output Representation using Deep Conditional Generative Models[[pdf]](http://papers.nips.cc/paper/5775-learning-structured-output-representation-using-deep-conditional-generative-models.pdf)[[code]](https://github.com/RuiShu/cvae)

- Tutorial on Variational Autoencoders[[pdf]](https://arxiv.org/pdf/1606.05908.pdf)[[code]](https://github.com/cdoersch/vae_tutorial)

## VAE-Summarization

- EMNLP2016-Language as a Latent Variable: Discrete Generative Models for Sentence Compression[[pdf]](https://arxiv.org/pdf/1609.07317.pdf)

## VAE-Dialogue

- A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues[[pdf]](https://arxiv.org/pdf/1605.06069.pdf)

- ACL2017-Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders[[pdf]](https://arxiv.org/pdf/1703.10960.pdf)[[code]](https://github.com/snakeztc/NeuralDialog-CVAE)

## VAE-Machine Translation

- EMNLP2016-Variational Neural Machine Translation[[pdf]](https://arxiv.org/pdf/1605.07869.pdf)

## VAE-Others

- ICLR2016-Generating Sentences from a Continuous Space[[pdf]](https://arxiv.org/pdf/1511.06349.pdf)

- ICML2016-Neural Variational Inference for Text Processing[[pdf]](https://arxiv.org/pdf/1511.06038.pdf)

# GAN

## GAN-Theory

- Generative Adversarial Nets [[pdf]](http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)[[code]](https://github.com/goodfeli/adversarial)

- Energy-Based Generative Adversarial Network

[[pdf]](https://arxiv.org/pdf/1609.03126v2.pdf)[[code]](https://github.com/buriburisuri/ebgan)

- Conditional generative adversarial nets [[pdf]](https://arxiv.org/abs/1411.1784)[[code]](https://github.com/zhangqianhui/Conditional-GAN)

- Generative Visual Manipulation on the Natural Image Manifold [[pdf]](https://arxiv.org/abs/1609.03552)[[code]](https://github.com/junyanz/iGAN)

- Adversarial Autoencoders [[pdf]](https://arxiv.org/abs/1511.05644)[[code]](https://github.com/Naresh1318/Adversarial_Autoencoder)

## GAN-Training Skill

- Which Training Methods for GANs do actually Converge [[pdf]](https://arxiv.org/pdf/1801.04406.pdf)[[code]](https://github.com/LMescheder/GAN_stability)

- Improved Techniques for Training GANs[[pdf]](https://arxiv.org/abs/1609.04468)[[code]](https://github.com/openai/improved-gan)

- Towards Principled Methods for Training Generative Adversarial Networks[[pdf]](https://arxiv.org/abs/1701.04862)

- Least Squares Generative Adversarial Networks[[pdf]](https://arxiv.org/abs/1611.04076)[[code]](https://github.com/pfnet-research/chainer-LSGAN)

- Wasserstein GAN[pdf](https://arxiv.org/abs/1701.07875)[[code]](https://github.com/martinarjovsky/WassersteinGAN)

- Improved Training of Wasserstein GANs[[pdf]](https://arxiv.org/abs/1704.00028)[[code]](https://github.com/igul222/improved_wgan_training)

- Generalization and Equilibrium in Generative Adversarial Nets[[pdf]](https://arxiv.org/abs/1703.00573)

- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium[[pdf]](http://papers.nips.cc/paper/7240-gans-trained-by-a-two-t)[[code]](https://github.com/bioinf-jku/TTUR)

## GAN-Modeling

- Arxiv 2020-LocoGAN — Locally Convolutional GAN [[pdf]]( https://arxiv.org/pdf/2002.07897.pdf ) [[code]]( https://github.com/gmum/LocoGAN)

- ICML 2018- RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks  [[pdf]]( http://proceedings.mlr.press/v80/yoon18b/yoon18b.pdf )

- ICML 2018-JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets [[pdf]]( http://proceedings.mlr.press/v80/pu18a/pu18a.pdf )

- AAAI 2019-Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval [[pdf]]( https://aaai.org/ojs/index.php/AAAI/article/view/3783 )

- AAAI 2019-PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification [[pdf]]( https://aaai.org/ojs/index.php/AAAI/article/view/3830 )

- ICML 2018-Which Training Methods for GANs do actually Converge? [[pdf]]( http://proceedings.mlr.press/v80/mescheder18a/mescheder18a.pdf )

- ICML 2018-Improved Training of Generative Adversarial Networks Using Representative Features[[pdf]]( http://proceedings.mlr.press/v80/bang18a/bang18a.pdf )

- AAAI 2019-On-Line Adaptative Curriculum Learning for GANs [[pdf]](https://aaai.org/ojs/index.php/AAAI/article/view/4224 )

- AAAI 2019-Improving GAN with Neighbors Embedding and Gradient Matching [[pdf]](https://aaai.org/ojs/index.php/AAAI/article/view/4454 )

## GAN-Sequence GAN

- AAAI 2017-SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient [[pdf]](https://arxiv.org/pdf/1609.05473.pdf)

- GANs for sequence of discrete elements with the Gumbel-softmax distribution [[pdf]](https://arxiv.org/pdf/1611.04051.pdf)

- Maximum-Likelihood Augmented Discrete Generative Adversarial Networks[[pdf]](https://arxiv.org/pdf/1702.07983.pdf)

## GAN-Machine translation

- Adversarial Neural Machine Translation [[pdf]](https://arxiv.org/pdf/1704.06933.pdf)

- NAACL 2018-Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets[[pdf]](https://arxiv.org/pdf/1703.04887.pdf)

## GAN-Generation

- NIPS 2016-Generating Text via Adversarial Training[[pdf]](http://people.duke.edu/~yz196/pdf/textgan.pdf)

- ICML 2017-Adversarial Feature Matching for Text Generation[[pdf]](https://arxiv.org/pdf/1706.03850.pdf)

- ICLR 2018-MaskGAN: Better Text Generation via Filling in the ____[[pdf]](https://arxiv.org/pdf/1801.07736.pdf)

- AAAI 2018-Long Text Generation via Adversarial Training with Leaked Information[[pdf]](https://arxiv.org/pdf/1709.08624.pdf)

## GAN-Classification

- Detecting Deceptive Reviews using Generative Adversarial Networks[[pdf]](https://arxiv.org/pdf/1805.10364.pdf)

- GANs for Semi-Supervised Opinion Spam Detection[[pdf]](https://arxiv.org/pdf/1903.08289.pdf)

## GAN-Dialogue

- Adversarial Learning for Neural Dialogue Generation[[pdf]](https://arxiv.org/pdf/1701.06547.pdf)

## GAN- Other applications

- ArXiv 2020-Unsupervised Discovery of Interpretable Directions in the GAN Latent Space [[pdf]]( https://arxiv.org/pdf/2002.03754.pdf ) [[code]]( https://github.com/anvoynov/GANLatentDiscovery )

- ASP-DAC 2018-Intelligent corner synthesis via cycle-consistent generative adversarial networks for efficient validation of autonomous driving systems [[pdf]]( https://ieeexplore.ieee.org/document/8297275 )

- Arxiv 2020-FakeLocator: Robust Localization of GAN-Based Face Manipulations via Semantic Segmentation Networks with Bells and Whistles [[pdf]](https://arxiv.org/pdf/2001.09598.pdf)

- EECV 2018- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [[pdf]](https://arxiv.org/pdf/1809.00219.pdf)

- CVPR 2019- APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs [[pdf]]() [[code]](http://t.cn/AiuxyshO)

- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks [[pdf]](http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf)[[code]](https://github.com/witnessai/LAPGAN)

- Generative Adversarial Text to Image Synthesis [[pdf]](https://arxiv.org/pdf/1605.05396.pdf)[[code]](https://github.com/reedscot/icml2016)

# Meta-Learning

## ML-Genaration

- AAAI 2018-Learning to Generalize: Meta-Learning for Domain Generalization[[pdf]](https://arxiv.org/pdf/1710.03463.pdf)

- NeurIPS 2017-Gated Fast Weights for On-The-Fly Neural Program Generation[[pdf]](http://metalearning.ml/2017/papers/metalearn17_schlag.pdf)

- NAACL 2018-Natural Language to Structured Query Generation via Meta-Learning[[pdf]](https://arxiv.org/pdf/1803.02400.pdf)[[code]](https://github.com/Microsoft/PointerSQL)

- IJCAI 2019-Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems[[pdf]](https://arxiv.org/pdf/1905.05644.pdf)

## ML-Classification

- EMNLP 2019-Induction Networks for Few-Shot Text Classification[[pdf]](https://www.aclweb.org/anthology/D19-1403.pdf)

- EMNLP 2019-Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification[[pdf]](https://www.aclweb.org/anthology/D19-1401.pdf)[[code]](https://github.com/mihaela-bornea/low-shot-text-classification)

- NeurIPS 2018-Large Margin Meta-Learning for Few-Shot Classification[[pdf]](https://pdfs.semanticscholar.org/9146/ee0cd87a00992bdb49c7a7e23679120b160b.pdf)

- ICML 2018-Predicting hyperparameters from meta-features in binaryclassification problems [[pdf]](https://pdfs.semanticscholar.org/9fd4/d462700291125e229144fff2ffdae6d7b782.pdf)[[code]](https://github.com/AuthEceSoftEng/ads)

## ML-Machine-Translation

- EMNLP 2018-Meta-Learning for Low-Resource Neural Machine Translation[[pdf]](https://arxiv.org/pdf/1808.08437.pdf)[[code]](https://github.com/salesforce/nonauto-nmt)

- Computation and Language-Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning[[pdf]](https://arxiv.org/pdf/1909.05438.pdf)

## ML-Auto-Meachine-Learning

- ICML 2018-P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning[[pdf]](https://www.isi.edu/~gil/papers/gil-etal-automl18.pdf)[[code]](https://github.com/usc-isi-i2/dsbox-ta2/tree/master/python/dsbox/profiler/primitive)

- ICML 2018-Practical Automated Machine Learning for the AutoML Challenge[[pdf]](https://pdfs.semanticscholar.org/12b4/8ce5a5cb66fc92e8c5b7aa4a651bb4e98a55.pdf)

- ICML 2018-AlphaD3M: Machine Learning Pipeline Synthesis[[pdf]](https://pdfs.semanticscholar.org/46ff/1e407be3f9f83cca960cbc075c664e4e7450.pdf)

- ICML 2018-Towards Further Automation in AutoML[[pdf]](https://pdfs.semanticscholar.org/403f/2b41d5eed1f9b52eba68dfa97f526a1809eb.pdf)

- NIPS 2019-Unsupervised Curricula for Visual Meta-Reinforcement Learning[[pdf]](http://papers.nips.cc/paper/9238-unsupervised-curricula-for-visual-meta-reinforcement-learning.pdf)

## ML-Vision&Language

- EMNLP 2019-Towards Zero-shot Language Modeling[[pdf]](https://www.aclweb.org/anthology/D19-1288.pdf)

- CVPR 2019-Meta-Learning With Differentiable Convex Optimization[[pdf]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lee_Meta-Learning_With_Differentiable_Convex_Optimization_CVPR_2019_paper.pdf)[[code]](https://github.com/kjunelee/MetaOptNet)

- ICCV 2019-Meta-Learning to Detect Rare Objects[[pdf]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.pdf)[[code]](https://github.com/kjunelee/MetaOptNet)

- CVPR 2019-Meta-Transfer Learning for Few-Shot Learning[[pdf]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Meta-Transfer_Learning_for_Few-Shot_Learning_CVPR_2019_paper.pdf)[[code]](https://github.com/y2l/meta-transfer-learning-tensorflow)

- CVPR 2019-Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning[[pdf]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wortsman_Learning_to_Learn_How_to_Learn_Self-Adaptive_Visual_Navigation_Using_CVPR_2019_paper.pdf)[[code]](https://github.com/allenai/savn.)

- ICCV 2019-MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning[[pdf]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_MetaPruning_Meta_Learning_for_Automatic_Neural_Network_Channel_Pruning_ICCV_2019_paper.pdf)[[code]](https:github.com/liuzechun/MetaPruning)

## ML-Question-Answering

- ECCV 2018-Visual Question Answering as a Meta Learning Task[[pdf]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Damien_Teney_Visual_Question_Answering_ECCV_2018_paper.pdf)

## ML-Others

- AAAI 2019-A Meta-Learning Approach for Custom Model Trainingp[[pdf]](https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/5105/4978)

- NeurIPS 2017-Meta-learning for instance-level data association[[pdf]](http://metalearning.ml/2017/papers/metalearn17_clark.pdf)

- ICML 2017-Model-agnostic meta-learning for fast adaptation of deep networksp[[pdf]](https://dl.acm.org/doi/10.5555/3305381.3305498)[[code]](https://github.com/cbfinn/maml)

- ICML 2018-Scalable Meta-Learning for Bayesian Optimization using Ranking-Weighted Gaussian Process Ensembles[[pdf]](https://ml.informatik.uni-freiburg.de/papers/18-AUTOML-RGPE.pdf)[[code]](https://github.com/cbfinn/maml)

# Lifelong-Learning

## LLL-Survey

- AGI 2011-Machine lifelong learning: challenges and benefits for artificial general intelligence[[pdf]](https://www.researchgate.net/profile/Daniel_Silver/publication/221328970_Machine_Lifelong_Learning_Challenges_and_Benefits_for_Artificial_General_Intelligence/links/00463515d5bc70ed5c000000/Machine-Lifelong-Learning-Challenges-and-Benefits-for-Artificial-General-Intelligence.pdf)

- arxiv-Continual Lifelong Learning with Neural Networks: A Review[[pdf]](https://arxiv.org/pdf/1802.07569.pdf)

- book-Lifelong Machine Learning[[pdf]](https://www.cs.uic.edu/~liub/lifelong-machine-learning-draft.pdf)

## LLL-Theory

- AAAI Spring Symposium 2013-Lifelong Machine Learning Systems: Beyond Learning Algorithms[[pdf]](https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/download/5802/5977)

- arxiv-An Empirical Investigation of Catastrophic Forgetting in

Gradient-Based Neural Networks[[pdf]](https://arxiv.org/pdf/1312.6211.pdf)

- arxiv-Continual learning: A comparative study on how to defy forgetting in classification tasks[[pdf]](https://arxiv.org/pdf/1909.08383.pdf)

- arxiv-Three scenarios for continual learning[[pdf]](https://arxiv.org/pdf/1904.07734.pdf)[[code]](https://github.com/GMvandeVen/continual-learning)

## LLL-Approaches

- CVPR 2017-Expert Gate: Lifelong Learning with a Network of Experts[[pdf]](https://arxiv.org/pdf/1611.06194.pdf)

- NeurIPS 2017-Continual learning with deep generative replay[[pdf]](http://papers.nips.cc/paper/6892-continual-learning-with-deep-generative-replay.pdf)

- ICLR 2018-Brain-inspired model for incremental learning[[pdf]](https://arxiv.org/pdf/1711.10563.pdf)

- ICCV 2017-Encoder Based Lifelong Learning[[pdf]](https://arxiv.org/pdf/1704.01920.pdf)[[code]](https://github.com/rahafaljundi/Encoder-Based-Lifelong-learning)

- NeurIPS 2018-Reinforced Continual Learning[[pdf]](http://papers.nips.cc/paper/7369-reinforced-continual-learning.pdf)

- ICML 2017-Continual Learning Through Synaptic Intelligence[[pdf]](https://arxiv.org/pdf/1703.04200.pdf)[[code]](https://github.com/ganguli-lab/pathint)

- NeurIPS 2019-Experience Replay for Continual Learning[[pdf]](http://papers.nips.cc/paper/8327-experience-replay-for-continual-learning.pdf)

- ICLR 2019-Learning to learn without forgetting by maximizing transfer and minimizing interference[[pdf]](https://openreview.net/pdf?id=B1gTShAct7)[[code]](https://github.com/mattriemer/MER)

# Graph-Neural-Networks

## GNN-Survey

- arxiv 2018-Graph Neural Networks: A Review of Methods and Applications[[pdf]](https://arxiv.org/pdf/1812.08434.pdf)

- arxiv 2019-A Comprehensive Survey on Graph Neural Networks[[pdf]](https://arxiv.org/pdf/1901.00596.pdf)

- arxiv 2018-Deep Learning on Graphs: A Survey[[pdf]](https://arxiv.org/pdf/1812.04202.pdf)

## GNN-Types

- ICLR 2019-DyRep: Learning Representations over Dynamic Graphs[[pdf]](https://openreview.net/pdf?id=HyePrhR5KX)

- AAAI 2019-Hypergraph Neural Networks[[pdf]](https://arxiv.org/pdf/1809.09401.pdf)

- WWW 2019-Heterogeneous Graph Attention Network[[pdf]](https://arxiv.org/pdf/1903.07293.pdf)

- IJCAI 2019-GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks[[pdf]](https://arxiv.org/pdf/1902.09817.pdf)

- NeurIPS 2019-HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs[[pdf]](http://papers.nips.cc/paper/8430-hypergcn-a-new-method-for-training-graph-convolutional-networks-on-hypergraphs)

- ICLR 2020-Composition-based Multi-Relational Graph Convolutional Networks[[pdf]](https://openreview.net/pdf?id=BylA_C4tPr)

## GNN-NLP

- AAAI 2019-Graph Convolutional Networks for Text Classification[[pdf]](https://arxiv.org/abs/1809.05679)

- ACL 2019-Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network[[pdf]](https://arxiv.org/abs/1906.04684)

- TACL 2018-Conversation Modeling on Reddit using a Graph-Structured LSTM[[pdf]](https://arxiv.org/pdf/1704.02080.pdf)

- EMNLP 2017-Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling[[pdf]](https://arxiv.org/abs/1703.04826)

- AAAI 2018-Graph Convolutional Networks with Argument-Aware Pooling for Event Detection[[pdf]](http://ix.cs.uoregon.edu/~thien/pubs/graphConv.pdf)

- NAACL 2018-Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks[[pdf]](https://www.aclweb.org/anthology/N18-2078/)

- ACL 2018-Graph-to-Sequence Learning using Gated Graph Neural Networks[[pdf]](https://arxiv.org/pdf/1806.09835.pdf)

- AAAI 2019-Graph Convolutional Networks for Text Classification[[pdf]](https://arxiv.org/pdf/1809.05679.pdf)

## GNN-CV

- CVPR 2017-The More You Know: Using Knowledge Graphs for Image Classification[[pdf]](https://arxiv.org/pdf/1612.04844.pdf)

- CVPR 2018-Dynamic Graph CNN for Learning on Point Clouds[[pdf]](https://arxiv.org/pdf/1801.07829.pdf)

- CVPR 2018-PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation[[pdf]](https://arxiv.org/pdf/1612.00593.pdf)

- IJCAI 2018-Deep Reasoning with Knowledge Graph for Social Relationship Understanding[[pdf]](https://arxiv.org/pdf/1807.00504.pdf)