Michael J. Paul

Artificial Intelligence · 2011–2020

A researcher in the Science corpus: 10 papers; trusts 112 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #21,010 of 35,345 by trust here, #18,186 by citations
• Natural Language Processing — 85 citations into it; #4,140 of 5,688 by trust here, #3,687 by citations
• Epidemiology — 25 citations into it; #133 of 2,148 by trust here, #703 by citations
• Sociology and Political Science — 23 citations into it
• Representation & Self-Supervised Learning — 21 citations into it; #2,386 of 3,145 by trust here, #3,009 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Safety, Robustness & Interpretability — 17 citations into it
• Statistical and Nonlinear Physics — 16 citations into it

Whom Michael J. Paul trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Tom Minka
3. Geoffrey E. Hinton
4. Yoon Kim
5. Steven Bird
6. Yoshua Bengio
7. Ilya Sutskever
8. Tomas Mikolov
9. Brendan T. O'Connor
10. Alex Krizhevsky

Who trusts Michael J. Paul overall

1. M. de Choudhury
2. Mark Dredze
3. Glen A. Coppersmith
4. Emre Kıcıman
5. Alex Lamb
6. Sofiane Abbar
7. Mrinal Kumar
8. Hamed Jelodar
9. David Jurgens
10. Scott Counts

Papers from Michael J. Paul's perspective

Ordered by how strongly the researchers Michael J. Paul trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (2019) — 41 citations
• Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (2020) — 105 citations
• You Are What You Tweet: Analyzing Twitter for Public Health (2011) — 1,145 citations
• Interpretable Machine Learning : Lessons from Topic Modeling (2016)
• Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance (2015) — 426 citations
• Machine learning:Trends, perspectives, and prospects (2015) — 61 citations
• National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic (2013) — 470 citations
• Separating Fact from Fear: Tracking Flu Infections on Twitter (2013) — 322 citations
• Hierarchical Bayesian Models for Latent Attribute Detection in Social Media (2011) — 89 citations
• Conference on Empirical Methods in Natural Language Processing Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media Socialnlp@emnlp2016 Chairs' Welcome Identifying and Categorizing Disaster-related Tweets Why Do They Leave: Modeling Participation in Online