Bowen Jing

Molecular Biology · 2020–2022

A researcher in the Science corpus: 6 papers; trusts 87 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 109 citations into it; #10,163 of 17,773 by trust here, #6,535 by citations
• Computational Theory and Mathematics — 67 citations into it; #10,277 of 12,182 by trust here, #2,015 by citations
• Materials Chemistry — 67 citations into it; #8,248 of 11,513 by trust here, #6,473 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #18,973 of 21,575 by trust here, #19,310 by citations
• Generative Models — 36 citations into it; #1,579 of 1,956 by trust here, #1,134 by citations
• Artificial Intelligence — 30 citations into it
• Computational Chemistry & Drug Discovery — 25 citations into it
• Computational Biology & Bioinformatics — 22 citations into it; #549 of 660 by trust here, #484 by citations

Whom Bowen Jing trusts overall

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

1. Yang Song
2. Diederik P. Kingma
3. Radford M. Neal
4. Jonathan Ho
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Alex Krizhevsky
8. Arash Vahdat
9. Stefano Ermon
10. Yann LeCun

Who trusts Bowen Jing overall

1. Zhuoran Qiao
2. Chloe Hsu
3. Kevin Kaichuang Yang
4. Hannes Stärk
5. Troy Luhman
6. Gabriele Corso
7. Zhangyang Gao
8. Shuo Zhang
9. Weili Nie
10. Erik Nijkamp

Papers from Bowen Jing's perspective

Ordered by how strongly the researchers Bowen Jing 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Subspace Diffusion Generative Models (2022) — 92 citations
• Learning from Protein Structure with Geometric Vector Perceptrons (2020) — 664 citations
• Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes (2020) — 68 citations
• DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022) — 790 citations
• Torsional Diffusion for Molecular Conformer Generation (2022) — 391 citations
• Equivariant Graph Neural Networks for 3D Macromolecular Structure (2021) — 129 citations