Brian Thompson

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 206 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 150 citations into it; #11,502 of 35,345 by trust here, #10,319 by citations
• Natural Language Processing — 144 citations into it; #1,385 of 5,688 by trust here, #2,203 by citations
• Computer Vision and Pattern Recognition — 63 citations into it; #7,969 of 21,575 by trust here, #8,952 by citations
• Evaluation & Benchmarks — 48 citations into it; #795 of 3,147 by trust here, #2,330 by citations
• Optimization & Training — 16 citations into it
• Machine Translation — 15 citations into it; #160 of 292 by trust here, #150 by citations
• Model Architectures — 13 citations into it
• Language and Linguistics — 9 citations into it

Whom Brian Thompson trusts overall

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

1. Philipp Koehn
2. Holger Schwenk
3. Yoshua Bengio
4. Rico Sennrich
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Ilya Sutskever
8. Dzmitry Bahdanau
9. Alex Krizhevsky
10. Kyunghyun Cho

Who trusts Brian Thompson overall

1. Markus Freitag
2. Danielle Saunders
3. Felix Stahlberg
4. Shuhao Gu
5. Thibault Sellam
6. Sebastian Gehrmann
7. Nitika Mathur
8. Weizhe Yuan
9. James Thorne
10. Yang Feng

Papers from Brian Thompson's perspective

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

Their papers

• ParaCrawl: Web-Scale Acquisition of Parallel Corpora (2020) — 296 citations
• Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation (2019) — 146 citations
• Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation (2018) — 56 citations
• Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing (2020) — 210 citations
• Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation (2018) — 64 citations
• The AFRL-MITLL WMT16 News-Translation Task Systems (2016) — 5 citations
• The AFRL-MITLL WMT15 System: There’s More than One Way to Decode It! (2015) — 7 citations