Evgeny Matusov

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 4 papers; trusts 95 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #19,803 of 35,345 by trust here, #19,018 by citations
• Natural Language Processing — 61 citations into it; #5,073 of 5,688 by trust here, #4,449 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #18,586 of 21,575 by trust here, #15,162 by citations
• Reinforcement Learning — 19 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Management Science and Operations Research — 12 citations into it
• Model Architectures — 11 citations into it

Whom Evgeny Matusov trusts overall

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

1. Ilya Sutskever
2. Yoshua Bengio
3. Rico Sennrich
4. Kyunghyun Cho
5. Franz Joseph Och
6. Oriol Vinyals
7. Geoffrey E. Hinton
8. Matthew G. Snover
9. Dzmitry Bahdanau
10. Kaiming He

Who trusts Evgeny Matusov overall

1. Antonios Anastasopoulos
2. Ron J. Weiss
3. Ye Jia
4. Julia Kreutzer
5. Ann Lee
6. David Chiang
7. Jan Chorowski
8. Melvin Johnson

Papers from Evgeny Matusov's perspective

Ordered by how strongly the researchers Evgeny Matusov 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Can Neural Machine Translation be Improved with User Feedback? (2018) — 101 citations
• On the integration of speech recognition and statistical machine translation (2005) — 104 citations
• Learning from Chunk-based Feedback in Neural Machine Translation (2018) — 19 citations
• Using Images to Improve Machine-Translating E-Commerce Product Listings. (2017) — 27 citations