Kelly W. Zhang

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 219 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #6,392 of 35,345 by trust here, #13,353 by citations
• Computer Vision and Pattern Recognition — 41 citations into it
• Natural Language Processing — 41 citations into it; #1,412 of 5,688 by trust here, #2,846 by citations
• Generative Models — 31 citations into it; #1,663 of 1,956 by trust here, #1,110 by citations
• Evaluation & Benchmarks — 11 citations into it
• Model Architectures — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Kelly W. Zhang trusts overall

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

1. Yoshua Bengio
2. Sepp Hochreiter
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yonatan Belinkov
6. J. Urgen Schmidhuber
7. Mitchell P. Marcus
8. Dzmitry Bahdanau
9. Ilya Sutskever
10. Alex Krizhevsky

Who trusts Kelly W. Zhang overall

1. Yonatan Belinkov
2. Elena Voita
3. Julian Michael
4. Andreas Madsen
5. Steven Cao
6. Ganesh Jawahar
7. Sebastian Ruder
8. Grzegorz Chrupała
9. Matthew E. Peters
10. Sneha Kudugunta

Papers from Kelly W. Zhang's perspective

Ordered by how strongly the researchers Kelly W. Zhang 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. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis (2018) — 71 citations
• Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis (2018) — 86 citations
• Adversarially Regularized Autoencoders (2017) — 300 citations