Jing He

Artificial Intelligence · with Bachman · 2016

A researcher in the Science corpus: 3 papers; trusts 37 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #13,515 of 35,345 by trust here, #10,065 by citations
• Natural Language Processing — 38 citations into it; #2,534 of 5,688 by trust here, #2,220 by citations
• Reinforcement Learning — 22 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it; #12,212 of 21,575 by trust here, #8,296 by citations
• Model Architectures — 11 citations into it

Whom Jing He trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Oriol Vinyals
5. Richard S. Sutton
6. Alex Krizhevsky
7. Ronald J. Williams
8. Diederik P. Kingma
9. Tomas Mikolov
10. Dzmitry Bahdanau

Who trusts Jing He overall

1. Jekaterina Novikova
2. Ondrej Dusek
3. Zachary Chase Lipton
4. Verena Rieser
5. Shuohang Wang
6. Shikhar Sharma

Papers from Jing He's perspective

Ordered by how strongly the researchers Jing He 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Policy Networks with Two-Stage Training for Dialogue Systems (2016) — 110 citations
• A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data (2016) — 47 citations
• Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data (2016) — 31 citations