Zhaohan Daniel Guo

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 166 researchers and is trusted by 64 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 128 citations into it; #25,665 of 35,345 by trust here, #2,388 by citations
• Computer Vision and Pattern Recognition — 91 citations into it; #4,003 of 21,575 by trust here, #2,040 by citations
• Reinforcement Learning — 70 citations into it; #2,045 of 2,242 by trust here, #1,371 by citations
• Representation & Self-Supervised Learning — 59 citations into it
• Management Science and Operations Research — 22 citations into it
• Model Architectures — 21 citations into it
• Optimization & Training — 19 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it

Whom Zhaohan Daniel Guo trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Kaiming He
6. Michael L. Littman
7. Alex Krizhevsky
8. Ilya Sutskever
9. Sham M. Kakade
10. Lihong Li

Who trusts Zhaohan Daniel Guo overall

1. Yash Chandak
2. Ioana Bica
3. Alberto Maria Metelli
4. Ziyang Tang
5. Danilo Jimenez Rezende
6. Ron Dorfman
7. Karol Gregor
8. Kuo-Hao Zeng
9. Jiafei Duan
10. Amy Zhang

Papers from Zhaohan Daniel Guo's perspective

Ordered by how strongly the researchers Zhaohan Daniel Guo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020) — 8,797 citations
• Neural Predictive Belief Representations (2018) — 82 citations
• Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning (2020) — 158 citations
• Concurrent PAC RL (2015) — 34 citations
• Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation (2017) — 42 citations