Yangchen Pan

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 3 papers; trusts 85 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it
• Reinforcement Learning — 54 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it
• Meta & Continual Learning — 20 citations into it
• Optimization & Training — 12 citations into it
• Generative Models — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Yangchen Pan trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Longxin Lin
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Andrew Barto
8. Yann LeCun
9. Ronald J. Williams
10. J. Urgen Schmidhuber

Who trusts Yangchen Pan overall

1. Rishabh Agarwal
2. Che Wang
3. Dale Schuurmans
4. Arsenii Kuznetsov
5. Qingfeng Lan

Papers from Yangchen Pan's perspective

Ordered by how strongly the researchers Yangchen Pan 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains (2018) — 49 citations
• Memory-efficient Reinforcement Learning with Knowledge Consolidation (2022) — 4 citations
• Maxmin Q-learning: Controlling the Estimation Bias of Q-learning (2020) — 219 citations