Yiding Jiang

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 113 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #15,943 of 35,345 by trust here, #8,077 by citations
• Reinforcement Learning — 59 citations into it; #1,574 of 2,242 by trust here, #1,300 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #15,027 of 21,575 by trust here, #16,787 by citations
• Scaling & Learning Theory — 37 citations into it
• Optimization & Training — 29 citations into it
• Statistical Learning Theory — 21 citations into it
• Control and Systems Engineering — 16 citations into it
• Computational Mechanics — 12 citations into it

Whom Yiding Jiang trusts overall

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

1. Peter L. Bartlett
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Behnam Neyshabur
5. David A. McAllester
6. Yoshua Bengio
7. Ilya Sutskever
8. Kaiming He
9. Alex Krizhevsky
10. Richard S. Sutton

Who trusts Yiding Jiang overall

1. Michael Janner
2. Andrew Kyle Lampinen
3. Zhiwei Jia
4. George Philipp
5. Felix Hill
6. Konstantin Schürholt
7. Suraj Nair
8. Wesley J. Maddox
9. Behnam Neyshabur
10. Pierre Foret

Papers from Yiding Jiang's perspective

Ordered by how strongly the researchers Yiding Jiang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Language as an Abstraction for Hierarchical Deep Reinforcement Learning (2019) — 248 citations
• Fantastic Generalization Measures and Where to Find Them (2019) — 740 citations
• Predicting the Generalization Gap in Deep Networks with Margin Distributions (2018) — 224 citations
• Observational Overfitting in Reinforcement Learning (2019) — 150 citations