Yatao Bian

Molecular Biology · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 36 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 76 citations into it
• Materials Chemistry — 63 citations into it
• Computational Theory and Mathematics — 59 citations into it; #10,617 of 12,182 by trust here, #7,851 by citations
• Artificial Intelligence — 45 citations into it; #30,638 of 35,345 by trust here, #31,361 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Graph Learning — 23 citations into it
• Model Architectures — 22 citations into it
• Computational Biology & Bioinformatics — 19 citations into it

Whom Yatao Bian trusts overall

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

1. Andreas Krause
2. Diederik P. Kingma
3. Maxim Sviridenko
4. Geoffrey E. Hinton
5. Max Welling
6. Yoshua Bengio
7. Alex Kulesza
8. Abhimanyu Das
9. Alex Krizhevsky
10. Jimmy Ba

Who trusts Yatao Bian overall

1. Sharon Qian
2. Yaron Singer
3. Shaked Brody

Papers from Yatao Bian's perspective

Ordered by how strongly the researchers Yatao Bian 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding (2021) — 11 citations
• Guarantees for Greedy Maximization of Non-submodular Functions with Applications (2017) — 267 citations
• Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021) — 174 citations
• Self-Supervised Graph Transformer on Large-Scale Molecular Data (2020) — 1,044 citations