Yifan Wu

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 112 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #3,531 of 35,345 by trust here, #9,641 by citations
• Reinforcement Learning — 82 citations into it; #395 of 2,242 by trust here, #952 by citations
• Management Science and Operations Research — 28 citations into it; #837 of 11,946 by trust here, #1,559 by citations
• Computational Theory and Mathematics — 20 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it; #2,343 of 3,145 by trust here, #2,910 by citations
• Statistics and Probability — 17 citations into it
• Electrical and Electronic Engineering — 13 citations into it; #618 of 16,539 by trust here, #5,200 by citations

Whom Yifan Wu trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. E. Todorov
5. Diederik P. Kingma
6. Sergey Levine
7. Alex Krizhevsky
8. Scott Fujimoto
9. Andrew Y. Ng
10. Yann LeCun

Who trusts Yifan Wu overall

1. Sergey Levine
2. Aviral Kumar
3. Ofir Nachum
4. Ilya Kostrikov
5. Tianhe Yu
6. Benjamin Eysenbach
7. George Tucker
8. Ruosong Wang
9. Ruslan Salakhutdinov
10. Justin Fu

Papers from Yifan Wu's perspective

Ordered by how strongly the researchers Yifan Wu 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
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

• Behavior Regularized Offline Reinforcement Learning (2019) — 863 citations
• The Laplacian in RL: Learning Representations with Efficient Approximations (2018) — 119 citations
• Instabilities of Offline RL with Pre-Trained Neural Representation (2021) — 46 citations
• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment (2019) — 133 citations