John DeNero

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 76 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #8,765 of 35,345 by trust here, #13,760 by citations
• Natural Language Processing — 58 citations into it; #1,764 of 5,688 by trust here, #3,421 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #7,226 of 21,575 by trust here, #13,815 by citations
• Reinforcement Learning — 23 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 11 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom John DeNero trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Dzmitry Bahdanau
5. Tomas Mikolov
6. Alex Krizhevsky
7. Kaiming He
8. Diederik P. Kingma
9. Kyunghyun Cho
10. J. Urgen Schmidhuber

Who trusts John DeNero overall

1. Felix Stahlberg
2. Jiameng Gao
3. Tamali Banerjee
4. Rico Sennrich
5. Ivan Provilkov
6. Timo Schick
7. Eva Hasler
8. Pushpak Bhattacharyya

Papers from John DeNero's perspective

Ordered by how strongly the researchers John DeNero 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Guiding Policies with Language via Meta-Learning (2018) — 67 citations
• Variable-Length Word Encodings for Neural Translation Models (2015) — 41 citations
• Models and Inference for Prefix-Constrained Machine Translation (2016) — 58 citations
• Compact Personalized Models for Neural Machine Translation (2018) — 56 citations