最近东北大学自然语言处理实验室在Github上发布了自然语言处理与机器学习最新综述论文合集,共有358篇之多,涵盖ML&nlp众多主题 , 是一份非常不错的指南!

地址: https://github.com/NiuTrans/ABigSurvey#architectures

在本文中,我们调研了数百篇关于自然语言处理(NLP)和机器学习(ML)的综述论文。我们将这些论文按热门话题分类,并对一些有趣的问题进行简单计算。此外,我们还显示了论文的url列表(358篇论文)。

A Survey of Surveys (NLP & ML)

Natural Language Processing Lab., School of Computer Science and Engineering, Northeastern University

NiuTrans Research

In this document, we survey hundreds of survey papers on Natural Language Processing (NLP) and Machine Learning (ML). We categorize these papers into popular topics and do simple counting for some interesting problems. In addition, we show the list of the papers with urls (358 papers).

Categorization

We follow the ACL and ICML submission guideline of recent years, covering a broad range of areas in NLP and ML. The categorization is as follows:

To reduce class imbalance, we separate some of the hot sub-topics from the original categorization of ACL and ICML submissions. E.g., NER is a first-level area in our categorization because it is the focus of several surveys.

Statistics

We show the number of paper in each area in Figures 1-2.

Figure 1: # of papers in each NLP area.

Figure 2: # of papers in each ML area..

Also, we plot paper number as a function of publication year (see Figure 3).

Figure 3: # of papers vs publication year.

In addition, we generate word clouds to show hot topics in these surveys (see Figures 4-5).

Figure 4: The word cloud for NLP.

Figure 5: The word cloud for ML.

The NLP Paper List

Computational Social Science and Social Media

  1. Computational Sociolinguistics: A Survey. Computational Linguistics 2016 paper

    Dong Nguyen, A Seza Dogruoz, Carolyn Penstein Rose, Franciska De Jong

Dialogue and Interactive Systems

  1. A Comparative Survey of Recent Natural Language Interfaces for Databases. VLDB 2019 paper

    Katrin Affolter, Kurt Stockinger, Abraham Bernstein

  2. A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message. arXiv 2015 paper

    AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith

  3. A Survey of Available Corpora for Building Data-Driven Dialogue Systems. arXiv 2015 paper

    Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau

  4. A Survey of Document Grounded Dialogue Systems. arXiv 2020 paper

    Longxuan Ma, Wei-Nan Zhang, Mingda Li, Ting Liu

  5. A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions. arXiv 2019 paper

    Sashank Santhanam, Samira Shaikh

  6. A Survey on Dialog Management: Recent Advances and Challenges. arXiv 2020 paper

    Yinpei Dai, Huihua Yu, Yixuan Jiang, Chengguang Tang, Yongbin Li, Jian Sun

  7. A Survey on Dialogue Systems: Recent Advances and New Frontiers. Sigkdd Explorations 2017 paper

    Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang

  8. Challenges in Building Intelligent Open-domain Dialog Systems. arXiv 2019 paper

    Minlie Huang, Xiaoyan Zhu, Jianfeng Gao

  9. Neural Approaches to Conversational AI. ACL 2018 paper

    Jianfeng Gao, Michel Galley, Lihong Li

  10. Recent Advances and Challenges in Task-oriented Dialog System. arXiv 2020 paper

    Zheng Zhang, Ryuichi Takanobu, Minlie Huang, Xiaoyan Zhu

Generation

  1. A bit of progress in language modeling. arXiv 2001 paper

    Joshua T. Goodman

  2. A Survey of Paraphrasing and Textual Entailment Methods. Journal of Artificial Intelligence Research 2010 paper

    Ion Androutsopoulos, Prodromos Malakasiotis

  3. A Survey on Neural Network Language Models. arXiv 2019 paper

    Kun Jing, Jungang Xu

  4. Neural Text Generation: Past, Present and Beyond. arXiv 2018 paper

    Sidi Lu, Yaoming Zhu, Weinan Zhang, Jun Wang, Yong Yu

  5. Pre-trained Models for Natural Language Processing : A Survey. arXiv 2020 paper

    Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, Xuanjing Huang

  6. Recent Advances in Neural Question Generation. arXiv 2019 paper

    Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan

  7. Recent Advances in SQL Query Generation: A Survey. arXiv 2020 paper

    Jovan Kalajdjieski, Martina Toshevska, Frosina Stojanovska

  8. Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research 2018 paper

    Albert Gatt,Emiel Krahmer

Information Extraction

  1. A Survey of Deep Learning Methods for Relation Extraction. arXiv 2017 paper

    Shantanu Kumar

  2. A Survey of Event Extraction From Text. IEEE 2019 paper

    Wei Xiang, Bang Wang

  3. A Survey of Neural Network Techniques for Feature Extraction from Text. arXiv 2017 paper

    Vineet John

  4. A Survey on Open Information Extraction. COLING 2018 paper

    Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh

  5. A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract). arXiv 2019 paper

    Artuur Leeuwenberg, Marie-Francine Moens

  6. Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey. arXiv 2016 paper

    Nabiha Asghar

  7. Content Selection in Data-to-Text Systems: A Survey. arXiv 2016 paper

    Dimitra Gkatzia

  8. Keyphrase Generation: A Multi-Aspect Survey. FRUCT 2019 paper

    Erion Cano, Ondrej Bojar

  9. More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction. arXiv 2020 paper

    Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou:

  10. Relation Extraction : A Survey. arXiv 2017 paper

    Sachin Pawar, Girish K. Palshikar, Pushpak Bhattacharyya

  11. Short Text Topic Modeling Techniques, Applications, and Performance: A Survey. arXiv 2019 paper

    Jipeng Qiang, Zhenyu Qian, Yun Li, Yunhao Yuan, Xindong Wu

Information Retrieval and Text Mining

  1. A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. arXiv 2017 paper

    Mehdi Allahyari, Seyed Amin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut

  2. A survey of methods to ease the development of highly multilingual text mining applications. language resources and evaluation 2012 paper

    Ralf Steinberger

  3. Opinion Mining and Analysis: A survey. IJNLC 2013 paper

    Arti Buche, M. B. Chandak, Akshay Zadgaonkar

Interpretability and Analysis of Models for NLP

  1. Analysis Methods in Neural Language Processing: A Survey. NACCL 2018 paper

    Yonatan Belinkov, James R. Glass

  2. Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop. EMNLP 2019 paper

    Afra Alishahi, Grzegorz Chrupala, Tal Linzen

  3. Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models. arXiv 2020 paper

    Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro

  4. Visualizing Natural Language Descriptions: A Survey. ACM 2016 paper

    Kaveh Hassani, Won-Sook Lee

  5. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?. ACL 2020 paper

    Kenneth Joseph, Jonathan H. Morgan

Knowledge Graph

  1. A survey of techniques for constructing chinese knowledge graphs and their applications. mdpi 2018 paper

    Tianxing Wu, Guilin Qi, Cheng Li, Meng Wang

  2. A Survey on Knowledge Graphs: Representation, Acquisition and Applications. arXiv 2020 paper

    Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu:

  3. Knowledge Graph Embedding for Link Prediction: A Comparative Analysis. arXiv 2016 paper

    Andrea Rossi, Donatella Firmani, Antonio Matinata, Paolo Merialdo, Denilson Barbosa

  4. Knowledge Graph Embedding: A Survey of Approaches and Applications. IEEE 2017 paper

    Quan Wang, Zhendong Mao, Bin Wang, Li Guo

  5. Knowledge Graphs. arXiv 2020 paper

    Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan F. Sequeda, Steffen Staab, Antoine Zimmermann

Language Grounding to Vision and Robotics and Beyond

  1. Emotionally-Aware Chatbots: A Survey. arXiv 2018 paper

    Endang Wahyu Pamungkas

  2. Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods. arXiv 2019 paper

    Aditya Mogadala, Marimuthu Kalimuthu, Dietrich Klakow

Linguistic Theories and Cognitive Modeling and Psycholinguistics

  1. Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing. Comput. Linguistics 45(3) 2019 paper

    Edoardo Maria Ponti, Helen O'Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Thierry Poibeau, Ekaterina Shutova, Anna Korhonen

  2. Survey on the Use of Typological Information in Natural Language Processing. COLING 2016 paper

    Helen O'Horan, Yevgeni Berzak, Ivan Vulic, Roi Reichart, Anna Korhonen

Machine Learning for NLP

  1. A comprehensive survey of mostly textual document segmentation algorithms since 2008. Pattern Recognition 2017 paper

    Sébastien Eskenazi, Petra Gomez-Kramer, Jean-Marc Ogier

  2. A Primer on Neural Network Models for Natural Language Processing. arXiv 2015 paper

    Yoav Goldberg

  3. A Survey Of Cross-lingual Word Embedding Models. Journal of Artificial Intelligence Research 2019 paper

    Sebastian Ruder, Ivan Vulic, Anders Sogaard

  4. A Survey of Neural Networks and Formal Languages. arXiv 2020 paper

    Joshua Ackerman, George Cybenko

  5. A Survey of the Usages of Deep Learning in Natural Language Processing. IEEE 2018 paper

    Daniel W. Otter, Julian R. Medina, Jugal K. Kalita

  6. A Survey on Contextual Embeddings. arXiv 2020 paper

    Qi Liu, Matt J. Kusner, Phil Blunsom

  7. Adversarial Attacks and Defense on Texts: A Survey. arXiv 2020 paper

    Aminul Huq, Mst. Tasnim Pervin

  8. Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey. arXiv 2019 paper

    Wei Emma Zhang, Quan Z Sheng, Ahoud Alhazmi, Chenliang Li

  9. An Introductory Survey on Attention Mechanisms in NLP Problems. IntelliSys 2019 paper

    Dichao Hu

  10. Attention in Natural Language Processing. arXiv 2019 paper

    Andrea Galassi, Marco Lippi, Paolo Torroni

  11. From static to dynamic word representations: a survey. ICMLC 2020 paper

    Yuxuan Wang, Yutai Hou, Wanxiang Che, Ting Liu

  12. From Word to Sense Embeddings: A Survey on Vector Representations of Meaning. Journal of Artificial Intelligence Research 2018 paper

    Jose Camachocollados, Mohammad Taher Pilehvar

  13. Natural Language Processing Advancements By Deep Learning: A Survey. arXiv 2020 paper

    Amirsina Torfi, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavvaf, Edward A. Fox

  14. Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering. COLING 2018 paper

    Wuwei Lan,Wei Xu

  15. Recent Trends in Deep Learning Based Natural Language Processing. IEEE 2018 paper

    Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria

  16. Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey. arXiv 2017 paper

    Lorenzo Ferrone, Fabio Massimo Zanzotto

  17. Towards a Robust Deep Neural Network in Texts: A Survey. arXiv 2020 paper

    Wenqi Wang, Lina Wang, Run Wang, Zhibo Wang, Aoshuang Ye

  18. Word Embeddings: A Survey. arXiv 2019 paper

    Felipe Almeida, Geraldo Xexéo

Machine Translation

  1. A Brief Survey of Multilingual Neural Machine Translation. arXiv 2019 paper

    Raj Dabre, Chenhui Chu, Anoop Kunchukuttan

  2. A Comprehensive Survey of Multilingual Neural Machine Translation. arXiv 2020 paper

    Raj Dabre, Chenhui Chu, Anoop Kunchukuttan

  3. A Survey of Deep Learning Techniques for Neural Machine Translation. arXiv 2020 paper

    Shuoheng Yang, Yuxin Wang, Xiaowen Chu

  4. A Survey of Domain Adaptation for Neural Machine Translation. COLING 2018 paper

    Chenhui Chu, Rui Wang

  5. A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation. arXiv 2019 paper

    Ilshat Gibadullin, Aidar Valeev, Albina Khusainova, Adil Mehmood Khan

  6. A Survey of Multilingual Neural Machine Translation. arXiv 2020 paper

    Raj Dabre, Chenhui Chu, Anoop Kunchukuttan

  7. A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena. Comput Linguistics 2016 paper

    Arianna Bisazza, Marcello Federico

  8. A Survey on Document-level Machine Translation: Methods and Evaluation. arXiv 2019 paper

    Sameen Maruf, Fahimeh Saleh, Gholamreza Haffari

  9. Machine Translation Approaches and Survey for Indian Languages. arXiv 2017 paper

    Nadeem Jadoon Khan, Waqas Anwar, Nadir Durrani

  10. Machine Translation Evaluation Resources and Methods: A Survey. arXiv 2018 paper

    Lifeng Han

  11. Machine Translation using Semantic Web Technologies: A Survey. Journal of Web Semantics 2018 paper

    Diego Moussallem, Matthias Wauer, Axelcyrille Ngonga Ngomo

  12. Machine-Translation History and Evolution: Survey for Arabic-English Translations. arXiv 2017 paper

    Nabeel T. Alsohybe, Neama Abdulaziz Dahan, Fadl Mutaher Baalwi

  13. Neural Machine Translation and Sequence-to-Sequence Models: A Tutorial. arXiv 2017 paper

    Graham Neubig

  14. Neural Machine Translation: A Review. arXiv 2019 paper

    Felix Stahlberg

  15. Neural Machine Translation: Challenges, Progress and Future. arXiv 2020 paper

    Jiajun Zhang, Chengqing Zong

  16. The Query Translation Landscape: a Survey. arXiv 2019 paper

    Mohamed Nadjib Mami, Damien Graux, Harsh Thakkar, Simon Scerri, Soren Auer, Jens Lehmann

Natural Language Processing

  1. A Survey and Classification of Controlled Natural Languages. Comput. Linguistics 2014 paper

    Tobias Kuhn

  2. Jumping NLP curves: A review of natural language processing research. IEEE 2014 paper

    Erik Cambria ; Bebo White

  3. Natural Language Processing - A Survey. arXiv 2012 paper

    Kevin Mote

  4. Natural Language Processing: State of The Art, Current Trends and Challenges. arXiv 2017 paper

    Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh

NER

  1. A survey of named entity recognition and classification. Lingvistic Investigationes 2007 paper

    David Nadeau, Satoshi Sekine

  2. A Survey of Named Entity Recognition in Assamese and other Indian Languages. arXiv 2014 paper

    Gitimoni Talukdar, Pranjal Protim Borah, Arup Baruah

  3. A Survey on Deep Learning for Named Entity Recognition. arXiv 2018 paper

    Jing Li, Aixin Sun, Jianglei Han, Chenliang Li

  4. A Survey on Recent Advances in Named Entity Recognition from Deep Learning models. COLING 2019 paper

    Vikas Yadav, Steven Bethard

  5. Design Challenges and Misconceptions in Neural Sequence Labeling. COLING 2018 paper

    Jie Yang, Shuailong Liang, Yue Zhang

  6. Neural Entity Linking: A Survey of Models based on Deep Learning. arXiv 2020 paper

    Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, Chris Biemann

NLP Applications

  1. A Comprehensive Survey of Grammar Error arXivection. arXiv 2020 paper

    Yu Wang, Yuelin Wang, Jie Liu, Zhuo Liu

  2. A Short Survey of Biomedical Relation Extraction Techniques. arXiv 2017 paper

    Elham Shahab

  3. A Survey on Natural Language Processing for Fake News Detection. LREC 2020 paper

    Ray Oshikawa, Jing Qian, William Yang Wang

  4. Automatic Language Identification in Texts: A Survey. J. Artif. Intell. Res. 65 2019 paper

    Tommi Jauhiainen

  5. Disinformation Detection: A review of linguistic feature selection and classification models in news veracity assessments. arXiv 2019 paper

    Jillian Tompkins

  6. Extraction and Analysis of Fictional Character Networks: A Survey. ACM 2019 paper

    Xavier Bost (LIA), Vincent Labatut (LIA)

  7. Fake News Detection using Stance Classification: A Survey. arXiv 2019 paper

    Anders Edelbo Lillie, Emil Refsgaard Middelboe

  8. Fake News: A Survey of Research, Detection Methods, and Opportunities. ACM 2018 paper

    Xinyi Zhou, Reza Zafarani

  9. Image Captioning based on Deep Learning Methods: A Survey. arXiv 2019 paper

    Yiyu Wang, Jungang Xu, Yingfei Sun, Ben He

  10. SECNLP: A Survey of Embeddings in Clinical Natural Language Processing. J. Biomed. Informatics 2019 paper

    Kalyan KS, S Sangeetha

  11. Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective. ACM 2019 paper

    Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris, C Raina MacIntyre

  12. Text Detection and Recognition in the Wild: A Review. arXiv 2020 paper

    Zobeir Raisi, Mohamed A. Naiel, Paul Fieguth, Steven Wardell, John Zelek

  13. Text Recognition in the Wild: A Survey. arXiv 2020 paper

    Xiaoxue Chen, Lianwen Jin, Yuanzhi Zhu, Canjie Luo, Tianwei Wang

Question Answering

  1. A survey on question answering technology from an information retrieval perspective. Information Sciences 2011 paper

    Oleksandr Kolomiyets, Marie-Francine Moens:

  2. A Survey on Why-Type Question Answering Systems. arXiv 2019 paper

    Manvi Breja, Sanjay Kumar Jain:

  3. Core techniques of question answering systems over knowledge bases: a survey. SpringerLink 2017 paper

    Dennis Diefenbach, Vanessa Lopez, Kamal Singh & Pierre Maret

  4. Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs. arXiv 2019 paper

    Nilesh Chakraborty,Denis Lukovnikov,Gaurav Maheshwari,Priyansh Trivedi,Jens Lehmann,Asja Fischer:

  5. Survey of Visual Question Answering: Datasets and Techniques. arXiv 2017 paper

    Akshay Kumar Gupta

  6. Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey. arXiv 2020 paper

    Zahra Abbasiyantaeb, Saeedeh Momtazi:

  7. Tutorial on Answering Questions about Images with Deep Learning. arXiv 2016 paper

    Mateusz Malinowski, Mario Fritz:

  8. Visual Question Answering using Deep Learning: A Survey and Performance Analysis. arXiv 2019 paper

    Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey, Snehasis Mukherjee:

Reading Comprehension

  1. A Survey on Machine Reading Comprehension Systems. arXiv 2020 paper

    Razieh Baradaran, Razieh Ghiasi, Hossein Amirkhani:

  2. A Survey on Neural Machine Reading Comprehension. arXiv 2019 paper

    Boyu Qiu, Xu Chen, Jungang Xu, Yingfei Sun:

  3. Machine Reading Comprehension: a Literature Review. arXiv 2019 paper

    Xin Zhang, An Yang, Sujian Li, Yizhong Wang

  4. Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond. arXiv 2020 paper

    Zhuosheng Zhang, Hai Zhao, Rui Wang

  5. Neural Machine Reading Comprehension: Methods and Trends. arXiv 2019 paper

    Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang:

Recommender Systems

  1. A review on deep learning for recommender systems: challenges and remedies. SpringerLink 2019 paper

    Zeynep Batmaz, Ali Yurekli, Alper Bilge, Cihan Kaleli:

  2. A Survey on Knowledge Graph-Based Recommender Systems. arXiv 2020 paper

    Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He

  3. Adversarial Machine Learning in Recommender Systems: State of the art and Challenges. ACM 2020 paper

    Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra

  4. Cross Domain Recommender Systems: A Systematic Literature Review. ACM 2017 paper

    Muhammad Murad Khan,Roliana Ibrahim,Imran Ghani

  5. Deep Learning based Recommender System: A Survey and New Perspectives. ACM 2019 paper

    Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay:

  6. Deep Learning on Knowledge Graph for Recommender System: A Survey. ACM 2020 paper

    Yang Gao, Yi-Fan Li, Yu Lin, Hang Gao, Latifur Khan

  7. Explainable Recommendation: A Survey and New Perspectives. arXiv 2020 paper

    Yongfeng Zhang, Xu Chen:

  8. Sequence-Aware Recommender Systems. ACM 2018 paper

    Massimo Quadrana,Paolo Cremonesi,Dietmar Jannach

  9. Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works. arXiv 2017 paper

    Ayush Singhal, Pradeep Sinha, Rakesh Pant:

Resources and Evaluation

  1. A Short Survey on Sense-Annotated Corpora. LREC 2020 paper

    Tommaso Pasini, José Camacho-Collados:

  2. A Survey of Current Datasets for Vision and Language Research. EMNLP 2015 paper

    Francis Ferraro, Nasrin Mostafazadeh, Ting-Hao (Kenneth) Huang, Lucy Vanderwende, Jacob Devlin, Michel Galley, Margaret Mitchell:

  3. A Survey of Word Embeddings Evaluation Methods. arXiv 2018 paper

    Amir Bakarov

  4. Critical Survey of the Freely Available Arabic Corpora. arXiv 2017 paper

    Wajdi Zaghouani:

  5. Distributional Measures of Semantic Distance: A Survey. arXiv 2012 paper

    Saif Mohammad, Graeme Hirst:

  6. Measuring Sentences Similarity: A Survey. Indian Journal of Science and Technology 2019 paper

    Mamdouh Farouk:

  7. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches. arXiv 2020 paper

    Shane Storks, Qiaozi Gao, Joyce Y. Chai

  8. Survey on Evaluation Methods for Dialogue Systems. arXiv 2019 paper

    Jan Deriu, álvaro Rodrigo, Arantxa Otegi, Guillermo Echegoyen, Sophie Rosset, Eneko Agirre, Mark Cieliebak:

  9. Survey on Publicly Available Sinhala Natural Language Processing Tools and Research. arXiv 2019 paper

    Nisansa de Silva

Semantics

  1. Diachronic word embeddings and semantic shifts: a survey. COLING 2018 paper

    Andrey Kutuzov, Lilja Ovrelid, Terrence Szymanski, Erik Velldal

  2. Evolution of Semantic Similarity -- A Survey. arXiv 2020 paper

    Dhivya Chandrasekaran, Vijay Mago

  3. Semantic search on text and knowledge bases. Foundations and trends in information retrieval 2016 paper

    Hannah Bast , Bjorn Buchhold, Elmar Haussmann

  4. Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature. arXiv 2014 paper

    Yarin Gal

  5. Survey of Computational Approaches to Lexical Semantic Change. arXiv 2019 paper

    Nina Tahmasebi, Lars Borin, Adam Jatowt

  6. Word sense disambiguation: a survey. ACM 2015 paper

    Alok Ranjan Pal, Diganta Saha

Sentiment Analysis and Stylistic Analysis and Argument Mining

  1. A Comprehensive Survey on Aspect Based Sentiment Analysis. arXiv 2020 paper

    Kaustubh Yadav

  2. A Survey on Sentiment and Emotion Analysis for Computational Literary Studies. arXiv 2018 paper

    Evgeny Kim, Roman Klinger

  3. Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research. arXiv 2020 paper

    Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Rada Mihalcea

  4. Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges. IEEE 2019 paper

    Jie Zhou, Jimmy Xiangji Huang, Qin Chen, Qinmin Vivian Hu, Tingting Wang, Liang He

  5. Deep Learning for Sentiment Analysis : A Survey. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery 2018 paper

    Lei Zhang, Shuai Wang, Bing Liu

  6. Sentiment analysis for Arabic language: A brief survey of approaches and techniques. arXiv 2018 paper

    Mo'ath Alrefai, Hossam Faris, Ibrahim Aljarah

  7. Sentiment Analysis of Czech Texts: An Algorithmic Survey. ICAART 2019 paper

    Erion Cano, Ondrej Bojar

  8. Sentiment Analysis of Twitter Data: A Survey of Techniques. arXiv 2016 paper

    Vishal.A.Kharde, Prof. Sheetal.Sonawane

  9. Sentiment Analysis on YouTube: A Brief Survey. arXiv 2015 paper

    Muhammad Zubair Asghar, Shakeel Ahmad, Afsana Marwat, Fazal Masud Kundi

  10. Sentiment/Subjectivity Analysis Survey for Languages other than English. Social Netw. Analys. Mining 2016 paper

    Mohammed Korayem, Khalifeh Aljadda, David Crandall

  11. Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey. arXiv 2019 paper

    Erion Cano, Maurizio Morisio

Speech and Multimodality

  1. A Comprehensive Survey on Cross-modal Retrieval. arXiv 2016 paper

    Kaiye Wang

  2. A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis. arXiv 2019 paper

    Jorge Agnese, Jonathan Herrera, Haicheng Tao, Xingquan Zhu

  3. A Survey of Code-switched Speech and Language Processing. arXiv 2019 paper

    Sunayana Sitaram, Khyathi Raghavi Chandu, Sai Krishna Rallabandi, Alan W. Black

  4. A Survey of Recent DNN Architectures on the TIMIT Phone Recognition Task. TSD 2018 paper

    Josef Michálek, Jan Vanek

  5. A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder. arXiv 2016 paper

    Hans Krupakar, Keerthika Rajvel, Bharathi B, Angel Deborah S, Vallidevi Krishnamurthy

  6. Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures. IJCAI 2017 paper

    Raffaella Bernardi, Ruket Cakici, Desmond Elliott, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis, Frank Keller, Adrian Muscat, Barbara Plank

  7. Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems. arXiv 2019 paper

    Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, Jannis Schuecker

  8. Multimodal Machine Learning: A Survey and Taxonomy. IEEE 2019 paper

    Tadas Baltrusaitis, Chaitanya Ahuja, Louis-Philippe Morency

  9. Speech and Language Processing. Stanford University 2019 paper

    Dan Jurafsky and James H. Martin

Summarization

  1. A Survey on Neural Network-Based Summarization Methods. arXiv 2018 paper

    Yue Dong

  2. Abstractive Summarization: A Survey of the State of the Art. AAAI 2019 paper

    Hui Lin, Vincent Ng

  3. Automated text summarisation and evidence-based medicine: A survey of two domains. arXiv 2017 paper

    Abeed Sarker, Diego Mollá Aliod, Cécile Paris

  4. Automatic Keyword Extraction for Text Summarization: A Survey. arXiv 2017 paper

    Santosh Kumar Bharti, Korra Sathya Babu

  5. From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information. arXiv 2020 paper

    Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

  6. Neural Abstractive Text Summarization with Sequence-to-Sequence Models: A Survey. arXiv 2018 paper

    Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy

  7. Recent automatic text summarization techniques: a survey. Artificial Intelligence Review 2016 paper

    Mahak Gambhir, Vishal Gupta

  8. Text Summarization Techniques: A Brief Survey. IJCAI 2017 paper

    Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut

Tagging Chunking Syntax and Parsing

  1. A Neural Entity Coreference Resolution Review. arXiv 2019 paper

    Nikolaos Stylianou, Ioannis Vlahavas

  2. A survey of cross-lingual features for zero-shot cross-lingual semantic parsing. arXiv 2019 paper

    Jingfeng Yang, Federico Fancellu, Bonnie L. Webber

  3. A Survey on Semantic Parsing. AKBC 2019 paper

    Aishwarya Kamath, Rajarshi Das

  4. The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers. arXiv 2018 paper

    Dongxiang Zhang, Lei Wang, Nuo Xu, Bing Tian Dai, Heng Tao Shen

Text Classification

  1. A Survey of Naive Bayes Machine Learning approach in Text Document Classification. IJCSIS 2010 paper

    K. A. Vidhya, G. Aghila

  2. A survey on phrase structure learning methods for text classification. IJNLC 2014 paper

    Reshma Prasad, Mary Priya Sebastian

  3. Deep Learning Based Text Classification: A Comprehensive Review. arXiv 2020 paper

    Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao

  4. Text Classification Algorithms: A Survey. arXiv 2019 paper

    Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura E. Barnes, Donald E. Brown

The ML Paper List

Architectures

  1. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. arXiv 2020 paper

    Zewen Li, Wenjie Yang, Shouheng Peng, Fan Liu

  2. A Survey of End-to-End Driving: Architectures and Training Methods. arXiv 2020 paper

    Ardi Tampuu, Maksym Semikin, Naveed Muhammad, Dmytro Fishman, Tambet Matiisen

  3. A Survey on Latent Tree Models and Applications. Journal of Artificial Intelligence Research 2013 paper

    Raphael Mourad, Christine Sinoquet, Nevin L. Zhang, Tengfei Liu, Philippe Leray

  4. An Attentive Survey of Attention Models. arXiv 2019 paper

    Sneha Chaudhari, Gungor Polatkan, Rohan Ramanath, Varun Mithal

  5. Binary Neural Networks: A Survey. Pattern Recognition 2020 paper

    Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, Nicu Sebe

  6. Deep Echo State Network (DeepESN): A Brief Survey. arXiv 2017 paper

    Claudio Gallicchio, Alessio Micheli

  7. Recent Advances in Convolutional Neural Networks. Pattern Recognition 2018 paper

    Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, Tsuhan Chen

  8. Sum-product networks: A survey. arXiv 2020 paper

    Iago París, Raquel Sánchez-Cauce, Francisco Javier Díez

  9. Survey on the attention based RNN model and its applications in computer vision. arXiv 2016 paper

    Feng Wang, David M. J. Tax

  10. Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks. arXiv 2019 paper

    Ralf C. Staudemeyer, Eric Rothstein Morris

AutoML

  1. A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. arXiv 2020 paper

    Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, Xin Wang

  2. A Survey on Neural Architecture Search. arXiv 2019 paper

    Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati

  3. AutoML: A Survey of the State-of-the-Art. arXiv 2019 paper

    Xin He, Kaiyong Zhao, Xiaowen Chu

  4. Benchmark and Survey of Automated Machine Learning Frameworks. arXiv 2020 paper

    Marc-André Zoller, Marco F. Huber

  5. Neural Architecture Search: A Survey. Journal of Machine Learning Research 2019 paper

    Thomas Elsken, Jan Hendrik Metzen, Frank Hutter

Bayesian Methods

  1. A survey of non-exchangeable priors for Bayesian nonparametric models. IEEE 2015 paper

    Nicholas J. Foti, Sinead Williamson

  2. Bayesian Nonparametric Space Partitions: A Survey. arXiv 2020 paper

    Xuhui Fan, Bin Li, Ling Luo, Scott A. Sisson

  3. Towards Bayesian Deep Learning: A Survey. arXiv 2016 paper

    Hao Wang, Dityan Yeung

Classification Clustering and Regression

  1. A Survey of Classification Techniques in the Area of Big Data. arXiv 2015 paper

    Praful Koturwar, Sheetal Girase, Debajyoti Mukhopadhyay

  2. A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges. arXiv 2020 paper

    Laura P. Swiler, Mamikon Gulian, Ari Frankel, Cosmin Safta, John D. Jakeman

  3. A Survey on Multi-View Clustering. arXiv 2017 paper

    Guoqing Chao, Shiliang Sun, Jinbo Bi

  4. Deep learning for time series classification: a review. arXiv 2019 paper

    Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller

  5. How Complex is your classification problem? A survey on measuring classification complexity. ACM 2019 paper

    Ana Carolina Lorena, Luis P F Garcia, Jens Lehmann, Marcilio C P Souto, Tin K Ho

Curriculum Learning

  1. Automatic Curriculum Learning For Deep RL: A Short Survey. arXiv 2020 paper

    Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, Pierre-Yves Oudeyer

  2. Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. arXiv 2020 paper

    Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone

Data Augmentation

  1. A survey on Image Data Augmentation for Deep Learning. Journal of Big Data 2019 paper

    Connor Shorten

  2. Time Series Data Augmentation for Deep Learning: A Survey. arXiv 2020 paper

    Qingsong Wen, Liang Sun, Xiaomin Song, Jingkun Gao, Xue Wang, Huan Xu

Deep Learning - General Methods

  1. A Survey of Neuromorphic Computing and Neural Networks in Hardware. arXiv 2017 paper

    Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank

  2. A Survey on Deep Hashing Methods. arXiv 2020 paper

    Xiao Luo, Chong Chen, Huasong Zhong, Hao Zhang, Minghua Deng, Jianqiang Huang, Xiansheng Hua

  3. A survey on modern trainable activation functions. arXiv 2020 paper

    Andrea Apicella, Francesco Donnarumma, Francesco Isgrò, Roberto Prevete

  4. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. IEEE 2020 paper

    Xiaofei Wang, Yiwen Han, Victor C.M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen

  5. Deep learning. nature 2015 paper

    Yann LeCun

  6. Deep Learning on Graphs: A Survey. IEEE 2018 paper

    Ziwei Zhang, Peng Cui, Wenwu Zhu

  7. Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective. arXiv 2019 paper

    Guan-Horng Liu, Evangelos A. Theodorou

  8. Geometric Deep Learning: Going beyond Euclidean data. IEEE 2016 paper

    Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst

  9. Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey. arXiv 2020 paper

    Andrea Borghesi, Federico Baldo, Michela Milano

  10. Review: Ordinary Differential Equations For Deep Learning. arXiv 2019 paper

    Xinshi Chen

  11. Survey of Dropout Methods for Deep Neural Networks. arXiv 2019 paper

    Alex Labach, Hojjat Salehinejad, Shahrokh Valaee

  12. Survey of Expressivity in Deep Neural Networks. arXiv 2016 paper

    Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, Jascha Sohldickstein

  13. Survey of reasoning using Neural networks. arXiv 2017 paper

    Amit Sahu

  14. The Deep Learning Compiler: A Comprehensive Survey. arXiv 2020 paper

    Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian

  15. The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches. arXiv 2018 paper

    Zahangir Alom, Tarek M Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S Awwal, Vijayan K Asari

  16. Time Series Forecasting With Deep Learning: A Survey. arXiv 2020 paper

    Bryan Lim, Stefan Zohren

Deep Reinforcement Learning

  1. A Brief Survey of Deep Reinforcement Learning. arXiv 2017 paper

    Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil A Bharath

  2. A Short Survey On Memory Based Reinforcement Learning. arXiv 2019 paper

    Dhruv Ramani

  3. A Short Survey on Probabilistic Reinforcement Learning. arXiv 2019 paper

    Reazul Hasan Russel

  4. A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress. arXiv 2018 paper

    Saurabh Arora, Prashant Doshi

  5. A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments. arXiv 2020 paper

    Sindhu Padakandla

  6. A Survey of Reinforcement Learning Informed by Natural Language. IJCAI 2019 paper

    Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktaschel

  7. A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions. arXiv 2020 paper

    Amit Kumar Mondal

  8. A survey on intrinsic motivation in reinforcement learning. arXiv 2019 paper

    Aubret, Arthur, Matignon, Laetitia, Hassas, Salima

  9. A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots. arXiv 2019 paper

    Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam

  10. Deep Reinforcement Learning: An Overview. arXiv 2017 paper

    Yuxi Li

  11. Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations. IEEE 2019 paper

    Dimitri P. Bertsekas

Federated Learning

  1. A Survey towards Federated Semi-supervised Learning. FRUCT 2020 paper

    Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang

  2. Advances and Open Problems in Federated Learning. arXiv 2019 paper

    Peter Kairouz, H Brendan Mcmahan, Brendan Avent, Aurelien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G L Doliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary A Garrett, Adria Gascon, Badih Ghazi, Phillip B Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecny, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrede Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Ozgur, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramer, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X Yu, Han Yu, Sen Zhao

  3. Threats to Federated Learning: A Survey. CoRL 2019 2020 paper

    Lingjuan Lyu, Han Yu, Qiang Yang

  4. Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective. arXiv 2020 paper

    Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang

Few-Shot and Zero-Shot Learning

  1. A Survey of Zero-Shot Learning: Settings, Methods, and Applications. ACM 2019 paper

    Wei Wang,Vincent W. Zheng,Han Yu,Chunyan Miao

  2. Few-shot Learning: A Survey. arXiv 2019 paper

    Yaqing Wang, Quanming Yao

  3. Generalizing from a Few Examples: A Survey on Few-Shot Learning. ACM 2019 paper

    Yaqing Wang, Quanming Yao, James Kwok, Lionel M. Ni

General Machine Learning

  1. A survey of dimensionality reduction techniques. arXiv 2014 paper

    C.O.S. Sorzano, J. Vargas, A. Pascual Montano

  2. A Survey of Predictive Modelling under Imbalanced Distributions. arXiv 2015 paper

    Paula Branco, Luis Torgo, Rita Ribeiro

  3. A Survey on Activation Functions and their relation with Xavier and He Normal Initialization. arXiv 2020 paper

    Leonid Datta

  4. A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective. arXiv 2018 paper

    Yuji Roh, Geon Heo, Steven Euijong Whang

  5. A survey on feature weighting based K-Means algorithms. Journal of Classification 2016 paper

    Renato Cordeiro de Amorim

  6. A Survey on Graph Kernels. Applied Network Science 2020 paper

    Nils M. Kriege, Fredrik D. Johansson, Christopher Morris

  7. A Survey on Multi-output Learning. IEEE 2019 paper

    Donna Xu, Yaxin Shi, Ivor W. Tsang, Yew-Soon Ong, Chen Gong, Xiaobo Shen

  8. A Survey on Resilient Machine Learning. arXiv 2017 paper

    Atul Kumar, Sameep Mehta

  9. A Survey on Surrogate Approaches to Non-negative Matrix Factorization. Vietnam journal of mathematics 2018 paper

    Pascal Fernsel, Peter Maass

  10. A Tutorial on Network Embeddings. arXiv 2018 paper

    Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena

  11. Adversarial Examples in Modern Machine Learning: A Review. arXiv 2019 paper

    Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, Archy de Berker

  12. Algorithms Inspired by Nature: A Survey. arXiv 2019 paper

    Pranshu Gupta

  13. Deep Tree Transductions - A Short Survey. INNSBDDL 2019 paper

    Davide Bacciu, Antonio Bruno

  14. Graph Representation Learning: A Survey. APSIPA Transactions on Signal and Information Processing 2019 paper

    Fenxiao Chen, Yuncheng Wang, Bin Wang, C.-C. Jay Kuo

  15. Heuristic design of fuzzy inference systems: A review of three decades of research. Engineering Applications of Artificial Intelligence 2019 paper

    Varun Ojha, Ajith Abraham, Vaclav Snasel

  16. Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results. arXiv 2013 paper

    Wenxin Jiang, Martin A. Tanner

  17. Hyperbox based machine learning algorithms: A comprehensive survey. arXiv 2019 paper

    Thanh Tung Khuat, Dymitr Ruta, Bogdan Gabrys

  18. Imbalance Problems in Object Detection: A Review. IEEE 2019 paper

    Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas

  19. Learning Representations of Graph Data -- A Survey. arXiv 2019 paper

    Mital Kinderkhedia

  20. Machine Learning at the Network Edge: A Survey. arXiv 2020 paper

    M.G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain

  21. Machine Learning for Spatiotemporal Sequence Forecasting: A Survey. arXiv 2018 paper

    Xingjian Shi, Dit-Yan Yeung

  22. Machine Learning in Network Centrality Measures: Tutorial and Outlook. Association for Computing Machinery 2018 paper

    Felipe Grando, Lisandro Zambenedetti Granville, Luís C. Lamb

  23. Machine Learning Testing: Survey, Landscapes and Horizons. arXiv 2019 paper

    Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu

  24. Machine Learning with World Knowledge: The Position and Survey. arXiv 2017 paper

    Yangqiu Song, Dan Roth

  25. Mean-Field Learning: a Survey. arXiv 2012 paper

    Hamidou Tembine, Raúl Tempone, Pedro Vilanova

  26. Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey. Autonomous Agents and Multi-Agent Systems 2020 paper

    Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé

  27. Narrative Science Systems: A Review. International Journal of Research in Computer Science 2015 paper

    Paramjot Kaur Sarao, Puneet Mittal, Rupinder Kaur

  28. Network Representation Learning: A Survey. IEEE 2020 paper

    Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

  29. Relational inductive biases, deep learning, and graph networks. arXiv 2018 paper

    Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gül?ehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu

  30. Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey. JMLR 2019 paper

    Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart

  31. Statistical Queries and Statistical Algorithms: Foundations and Applications. arXiv 2020 paper

    Lev Reyzin

  32. Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey. arXiv 2011 paper

    Yang Zhou

  33. Survey on Feature Selection. arXiv 2015 paper

    Tarek Amr Abdallah, Beatriz de La Iglesia

  34. Survey on Five Tribes of Machine Learning and the Main Algorithms. Software Guide 2019 paper

    LI Xu-ran, DING Xiao-hong

  35. Survey: Machine Learning in Production Rendering. arXiv 2020 paper

    Shilin Zhu

  36. The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses. arXiv 2018 paper

    Dirk Sudholt

  37. Tutorial on Variational Autoencoders. arXiv 2016 paper

    Carl Doersch

  38. Unsupervised Cross-Lingual Representation Learning. ACL 2019 paper

    Sebastian Ruder, Anders Sogaard, Ivan Vulic

  39. Verification for Machine Learning, Autonomy, and Neural Networks Survey. arXiv 2018 paper

    Weiming Xiang, Patrick Musau, Ayana A. Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld, Taylor T. Johnson

Generative Adversarial Networks

  1. A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications. arXiv 2020 paper

    Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, Jieping Ye

  2. A Survey on Generative Adversarial Networks: Variants, Applications, and Training. arXiv 2020 paper

    Abdul Jabbar, Xi Li, Bourahla Omar

  3. Generative Adversarial Networks: A Survey and Taxonomy. arXiv 2019 paper

    Zhengwei Wang, Qi She, Tomas E Ward

  4. Generative Adversarial Networks: An Overview. IEEE 2018 paper

    Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A Bharath

  5. How Generative Adversarial Nets and its variants Work: An Overview of GAN. arXiv 2017 paper

    Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon

  6. Stabilizing Generative Adversarial Network Training: A Survey. arXiv 2020 paper

    Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom

  7. Stabilizing Generative Adversarial Networks: A Survey. arXiv 2019 paper

    Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom

Graph Neural Networks

  1. A Comprehensive Survey on Graph Neural Networks. IEEE 2019 paper

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu

  2. A Survey on The Expressive Power of Graph Neural Networks. arXiv 2020 paper

    Ryoma Sato

  3. Adversarial Attack and Defense on Graph Data: A Survey. arXiv 2018 paper

    Lichao Sun, Ji Wang, Philip S. Yu, Bo Li

  4. Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks. arXiv 2020 paper

    Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Chang-Tien Lu

  5. Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey. arXiv 2020 paper

    Joakim Skarding, Bogdan Gabrys, Katarzyna Musial

  6. Graph embedding techniques, applications, and performance: A survey. Knowledge Based Systems 2017 paper

    Palash Goyal, Emilio Ferrara

  7. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. arXiv 2020 paper

    Luis C. Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, Moshe Vardi

  8. Graph Neural Networks: A Review of Methods and Applications. arXiv 2018 paper

    Maosong Sun, Zhengyan Zhang, Ganqu Cui, Cheng Yang, Jie Zhou, Zhiyuan Liu

  9. Introduction to Graph Neural Networks. IEEE 2020 paper

    Zhiyuan Liu, Jie Zhou

  10. Tackling Graphical NLP problems with Graph Recurrent Networks. arXiv 2019 paper

    Linfeng Song

Interpretability and Analysis

  1. A Survey Of Methods For Explaining Black Box Models. ACM 2018 paper

    Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi

  2. A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability. arXiv 2018 paper

    Xiaowei Huang

  3. Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation. Sigkdd Explorations 2020 paper

    Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu

  4. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Information Fusion 2020 paper

    Alejandro Barredo Arrieta, Natalia Diazrodriguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gillopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera

  5. Explainable Reinforcement Learning: A Survey. CD-MAKE 2020 2020 paper

    Erika Puiutta, Eric M. S. P. Veith

  6. Foundations of Explainable Knowledge-Enabled Systems. Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges/arXiv 2020 paper

    Shruthi Chari

  7. How Generative Adversarial Networks and Their Variants Work: An Overview. ACM 2017 paper

    Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon

  8. Language (Technology) is Power: A Critical Survey of "Bias" in NLP. Association for Computational Linguistics 2020 paper

    Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna Wallach

  9. Survey & Experiment: Towards the Learning Accuracy. arXiv 2010 paper

    Zeyuan Allen Zhu

  10. Understanding Neural Networks via Feature Visualization: A survey. arXiv 2019 paper

    Anh Nguyen, Jason Yosinski, Jeff Clune

  11. Visual interpretability for deep learning: a survey. Journal of Zhejiang University Science C 2018 paper

    Quanshi Zhang, Songchun Zhu

  12. Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons. CEC 2019 paper

    Huiru Gao, Haifeng Nie, Ke Li

Meta Learning

  1. A Comprehensive Overview and Survey of Recent Advances in Meta-Learning. arXiv 2020 paper

    Huimin Peng

  2. Meta-Learning in Neural Networks: A Survey. arXiv 2020 paper

    Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey

  3. Meta-Learning: A Survey. arXiv 2018 paper

    Joaquin Vanschoren

Metric Learning

  1. A Survey on Metric Learning for Feature Vectors and Structured Data. arXiv 2013 paper

    Aurélien Bellet, Amaury Habrard, Marc Sebban

  2. A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Experiments. arXiv 2018 paper

    Juan Luis Suárez, Salvador García, Francisco Herrera

ML Applications

  1. A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications. Neural Networks 2019 paper

    Leonardo Enzo Brito da Silva, Islam Elnabarawy, Donald C. Wunsch II

  2. A Survey of Machine Learning Methods and Challenges for Windows Malware Classification. arXiv 2020 paper

    Edward Raff, Charles Nicholas

  3. A survey on deep hashing for image retrieval. arXiv 2020 paper

    Xiaopeng Zhang

  4. A Survey on Deep Learning based Brain-Computer Interface: Recent Advances and New Frontiers. arXiv 2019 paper

    Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica J M Monaghan, David Mcalpine, Yu Zhang

  5. A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis 2017 paper

    Geert J S Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud A A Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A W M Van Der Laak, Bram Van Ginneken, Clara I Sanchez

  6. Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial. IEEE 2019 paper

    Mingzhe Chen, Ursula Challita, Walid Saad, Changchuan Yin, Mérouane Debbah

  7. How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature. arXiv 2018 paper

    Doris Xin, Litian Ma, Shuchen Song, Aditya G. Parameswaran

  8. Machine Learning Aided Static Malware Analysis: A Survey and Tutorial. arXiv 2018 paper

    Andrii Shalaginov, Sergii Banin, Ali Dehghantanha, Katrin Franke

  9. Machine Learning for Survival Analysis: A Survey. arXiv 2017 paper

    Ping Wang, Yan Li, Chandan K. Reddy

  10. The Creation and Detection of Deepfakes: A Survey. arXiv 2020 paper

    Yisroel Mirsky, Wenke Lee

Model Compression and Acceleration

  1. A Survey of Model Compression and Acceleration for Deep Neural Networks. arXiv 2017 paper

    Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang

  2. A Survey on Methods and Theories of Quantized Neural Networks. arXiv 2018 paper

    Yunhui Guo

  3. An Overview of Neural Network Compression. arXiv 2020 paper

    J O Neill

  4. Knowledge Distillation: A Survey. arXiv 2020 paper

    Jianping Gou, Baosheng Yu, Stephen John Maybank, Dacheng Tao

  5. Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey. arXiv 2020 paper

    Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah

Multi-Task and Multi-View Learning

  1. A Brief Review on Multi-Task Learning. Multimedia Tools and Applications 2018 paper

    Kimhan Thung, Chong Yaw Wee

  2. A Survey on Multi-Task Learning. arXiv 2017 paper

    Yu Zhang, Qiang Yang

  3. A Survey on Multi-view Learning. arXiv 2013 paper

    Chang Xu, Dacheng Tao, Chao Xu

  4. An overview of multi-task learning. National Science Review 2018 paper

    Yu Zhang, Qiang Yang

  5. An Overview of Multi-Task Learning in Deep Neural Networks. arXiv 2017 paper

    Sebastian Ruder

  6. Revisiting Multi-Task Learning in the Deep Learning Era. arXiv 2020 paper

    Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Dengxin Dai, Luc Van Gool

Online Learning

  1. A Survey of Algorithms and Analysis for Adaptive Online Learning. Journal of Machine Learning Research 2017 paper

    H. Brendan McMahan

  2. Online Learning: A Comprehensive Survey. arXiv 2018 paper

    Steven C.H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao

  3. Preference-based Online Learning with Dueling Bandits: A Survey. arXiv 2018 paper

    Robert Busa-Fekete, Eyke Hüllermeier, Adil El Mesaoudi-Paul

Optimization

  1. A Survey of Optimization Methods from a Machine Learning Perspective. arXiv 2019 paper

    Shiliang Sun, Zehui Cao, Han Zhu, Jing Zhao

  2. A Systematic and Meta-analysis Survey of Whale Optimization Algorithm. Comput. Intell. Neurosci. 2019 paper

    Hardi M. Mohammed, Shahla U. Umar, Tarik A. Rashid

  3. An overview of gradient descent optimization algorithms. arXiv 2017 paper

    Sebastian Ruder

  4. Convex Optimization Overview. IEEE 2008 paper

    Nikos Komodakis

  5. Gradient Boosting Machine: A Survey. arXiv 2019 paper

    Zhiyuan He, Danchen Lin, Thomas Lau, Mike Wu

  6. Optimization for deep learning: theory and algorithms. arXiv 2019 paper

    Ruoyu Sun

  7. Optimization Models for Machine Learning: A Survey. arXiv 2019 paper

    Claudio Gambella, Bissan Ghaddar, Joe Naoum-Sawaya

  8. Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Machine Learning and Knowledge Extraction 2019 paper

    Saptarshi Sengupta, Sanchita Basak, Richard Alan Peters II

Semi-Supervised and Unsupervised Learning

  1. A brief introduction to weakly supervised learning. arXiv 2018 paper

    Zhihua Zhou

  2. A Survey on Semi-Supervised Learning Techniques. arXiv 2014 paper

    V. Jothi Prakash, Dr. L.M. Nithya

  3. Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results. arXiv 2019 paper

    Alexander Mey, Marco Loog

  4. Learning from positive and unlabeled data: a survey. Machine Learning 2020 paper

    Jessa Bekker, Jesse Davis

Transfer Learning

  1. A Comprehensive Survey on Transfer Learning. arXiv 2019 paper

    Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, Qing He

  2. A Survey of Unsupervised Deep Domain Adaptation. arXiv 2020 paper

    Garrett Wilson, Diane J. Cook

  3. A Survey on Deep Transfer Learning. ICANN 2018 paper

    Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu

  4. A survey on domain adaptation theory: learning bounds and theoretical guarantees. arXiv 2020 paper

    Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani

  5. Evolution of transfer learning in natural language processing. arXiv 2019 paper

    Aditya Malte, Pratik Ratadiya

  6. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv 2019 paper

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu

  7. Neural Unsupervised Domain Adaptation in NLP---A Survey. arXiv 2020 paper

    Alan Ramponi, Barbara Plank

  8. Transfer Adaptation Learning: A Decade Survey. arXiv 2019 paper

    Lei Zhang

Trustworthy Machine Learning

  1. A Survey on Bias and Fairness in Machine Learning. arXiv 2019 paper

    Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan

  2. Differential Privacy and Machine Learning: a Survey and Review. arXiv 2014 paper

    Zhanglong Ji, Zachary C. Lipton, Charles Elkan

  3. Tutorial: Safe and Reliable Machine Learning. arXiv 2019 paper

    Suchi Saria, Adarsh Subbaswamy

Team Members

Ziyang Wang, Nuo Xu, Bei Li, Yinqiao Li, Quan Du, Tong Xiao, and Jingbo Zhu

Please feel free to contact us if you have any questions (wangziyang [at] stumail.neu.edu.cn or libei_neu [at] outlook.com).

We would like to thank the people who have contributed to this project. They are

Xin Zeng, Laohu Wang, Chenglong Wang, Xiaoqian Liu, Xuanjun Zhou, Jingnan Zhang, Yongyu Mu, Zefan Zhou, Yanhong Jiang, Xinyang Zhu, Xingyu Liu, Dong Bi, Ping Xu, Zijian Li, Fengning Tian, Hui Liu, Kai Feng, Yuhao Zhang, Chi Hu, Di Yang, Lei Zheng, Hexuan Chen, Zeyang Wang, Tengbo Liu, Xia Meng, Weiqiao Shan, Shuhan Zhou, Tao Zhou, Runzhe Cao, Yingfeng Luo, Binghao Wei, Wandi Xu, Yan Zhang, Yichao Wang, Mengyu Ma, Zihao Liu

成为VIP会员查看完整内容
0
37

相关内容

“机器学习是近20多年兴起的一门多领域交叉学科,涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。机器学习理论主要是设计和分析一些让 可以自动“ 学习”的算法。机器学习算法是一类从数据中自动分析获得规律,并利用规律对未知数据进行预测的算法。因为学习算法中涉及了大量的统计学理论,机器学习与统计推断学联系尤为密切,也被称为统计学习理论。算法设计方面,机器学习理论关注可以实现的,行之有效的学习算法。很多 推论问题属于 无程序可循难度,所以部分的机器学习研究是开发容易处理的近似算法。” ——中文维基百科

知识荟萃

精品入门和进阶教程、论文和代码整理等

更多

查看相关VIP内容、论文、资讯等

自然语言处理(NLP)帮助智能机器更好地理解人类语言,实现基于语言的人机交流。计算能力的最新发展和大量语言数据的出现,增加了使用数据驱动方法自动进行语义分析的需求。由于深度学习方法在计算机视觉、自动语音识别,特别是NLP等领域的应用取得了显著的进步,数据驱动策略的应用已经非常普遍。本调查对得益于深度学习的NLP的不同方面和应用进行了分类和讨论。它涵盖了核心的NLP任务和应用,并描述了深度学习方法和模型如何推进这些领域。我们进一步分析和比较不同的方法和最先进的模型。

成为VIP会员查看完整内容
0
73

When I started out, I had a strong quantitative background (chemical engineering undergrad, was taking PhD courses in chemical engineering) and some functional skills in programming. From there, I first dove deep into one type of machine learning (Gaussian processes) along with general ML practice (how to set up ML experiments in order to evaluate your models) because that was what I needed for my project. I learned mostly online and by reading papers, but I also took one class on data analysis for biologists that wasn’t ML-focused but did cover programming and statistical thinking. Later, I took a linear algebra class, an ML survey class, and an advanced topics class on structured learning at Caltech. Those helped me obtain a broad knowledge of ML, and then I’ve gained deeper understandings of some subfields that interest me or are especially relevant by reading papers closely (chasing down references and anything I don’t understand and/or implementing the core algorithms myself).

成为VIP会员查看完整内容
0
10
小贴士
相关VIP内容
专知会员服务
73+阅读 · 3月6日
【综述】7篇非常简洁近期深度学习综述论文
专知会员服务
53+阅读 · 2019年12月31日
【推荐系统/计算广告/机器学习/CTR预估资料汇总】
专知会员服务
39+阅读 · 2019年10月21日
深度学习自然语言处理综述,266篇参考文献
专知会员服务
89+阅读 · 2019年10月12日
Keras François Chollet 《Deep Learning with Python 》, 386页pdf
专知会员服务
29+阅读 · 2019年10月12日
机器学习入门的经验与建议
专知会员服务
10+阅读 · 2019年10月10日
学习自然语言处理路线图
专知会员服务
27+阅读 · 2019年9月24日
机器学习在材料科学中的应用综述,21页pdf
专知会员服务
9+阅读 · 2019年9月24日
相关资讯
中文自然语言处理相关资料集合指南
专知
14+阅读 · 2019年3月10日
深度学习自然语言处理阅读清单
专知
17+阅读 · 2019年1月13日
COLING 2018-最新论文最全分类-整理分享
深度学习与NLP
5+阅读 · 2018年7月6日
五个精彩实用的自然语言处理资源
机器学习研究会
5+阅读 · 2018年2月23日
Python机器学习教程资料/代码
机器学习研究会
4+阅读 · 2018年2月22日
自然语言处理 (NLP)资源大全
机械鸡
34+阅读 · 2017年9月17日
相关论文
Liang Chen,Jintang Li,Jiaying Peng,Tao Xie,Zengxu Cao,Kun Xu,Xiangnan He,Zibin Zheng
20+阅读 · 3月10日
Area Attention
Yang Li,Lukasz Kaiser,Samy Bengio,Si Si
4+阅读 · 2019年5月23日
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu,Shirui Pan,Fengwen Chen,Guodong Long,Chengqi Zhang,Philip S. Yu
5+阅读 · 2019年3月10日
Knowledge Representation Learning: A Quantitative Review
Yankai Lin,Xu Han,Ruobing Xie,Zhiyuan Liu,Maosong Sun
24+阅读 · 2018年12月28日
Ziwei Zhang,Peng Cui,Wenwu Zhu
34+阅读 · 2018年12月11日
Deep Reinforcement Learning: An Overview
Yuxi Li
8+阅读 · 2018年11月26日
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
Ruijie Wang,Yuchen Yan,Jialu Wang,Yuting Jia,Ye Zhang,Weinan Zhang,Xinbing Wang
3+阅读 · 2018年8月7日
Bryan McCann,James Bradbury,Caiming Xiong,Richard Socher
5+阅读 · 2018年6月20日
Markus Schedl,Hamed Zamani,Ching-Wei Chen,Yashar Deldjoo,Mehdi Elahi
5+阅读 · 2018年3月21日
Tadas Baltrušaitis,Chaitanya Ahuja,Louis-Philippe Morency
93+阅读 · 2017年8月1日
Top