Zhuoran Qiao

Molecular Biology · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 139 citations into it
• Materials Chemistry — 86 citations into it
• Computational Theory and Mathematics — 79 citations into it
• Artificial Intelligence — 35 citations into it
• Computational Biology & Bioinformatics — 32 citations into it
• Generative Models — 21 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Statistical and Nonlinear Physics — 13 citations into it

Whom Zhuoran Qiao trusts overall

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

1. Risi Kondor
2. Yang Zhang
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yang Song
6. Jonathan Ho
7. Jimmy Ba
8. Yoshua Bengio
9. Max Welling
10. Alex Krizhevsky

Who trusts Zhuoran Qiao overall

1. José Jiménez-Luna
2. Shuya Nakata
3. Yuanqi Du
4. Yoshiharu Mori
5. Clemens Isert
6. Francesca Grisoni
7. Tianfan Fu

Papers from Zhuoran Qiao's perspective

Ordered by how strongly the researchers Zhuoran Qiao 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features (2020) — 263 citations
• Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022) — 30 citations
• OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy. (2021) — 87 citations
• State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022) — 173 citations