Yufeng Su

Molecular Biology · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 100 citations into it
• Materials Chemistry — 48 citations into it
• Computational Biology & Bioinformatics — 44 citations into it
• Artificial Intelligence — 43 citations into it
• Generative Models — 29 citations into it
• Reinforcement Learning — 27 citations into it
• Computational Chemistry & Drug Discovery — 18 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it

Whom Yufeng Su trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Emiel Hoogeboom
4. S. Parasuraman
5. Max Welling
6. Yoshua Bengio
7. Michael L. Littman
8. Alex Krizhevsky
9. Richard S. Sutton
10. Jonathan Ho

Papers from Yufeng Su's perspective

Ordered by how strongly the researchers Yufeng Su 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. GENERATIVE ADVERSARIAL NETS
7. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (2022) — 305 citations
• Factorized Q-learning for large-scale multi-agent systems (2018) — 75 citations
• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023) — 326 citations
• Evolutionary context-integrated deep sequence modeling for protein engineering (2020) — 12 citations