Chence Shi

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 6 papers; trusts 86 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it
• Materials Chemistry — 72 citations into it; #2,088 of 11,513 by trust here, #5,212 by citations
• Computer Vision and Pattern Recognition — 56 citations into it
• Computational Theory and Mathematics — 55 citations into it; #9,425 of 12,182 by trust here, #1,700 by citations
• Graph Learning — 48 citations into it
• Generative Models — 45 citations into it; #898 of 1,956 by trust here, #899 by citations
• Molecular Biology — 45 citations into it; #3,565 of 17,773 by trust here, #6,028 by citations
• Probabilistic & Bayesian Methods — 30 citations into it

Whom Chence Shi trusts overall

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

1. Diederik P. Kingma
2. Max Welling
3. Yang Song
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Alex Krizhevsky
7. Jimmy Ba
8. Yann LeCun
9. Pascal Vincent
10. Kaiming He

Who trusts Chence Shi overall

1. Shitong Luo
2. Minkai Xu
3. Tian Xie
4. Yuanqi Du
5. Bowen Jing
6. Ling Yang
7. Chengxi Zang
8. Yuyang Wang
9. Regina Barzilay
10. Gengmo Zhou

Papers from Chence Shi's perspective

Ordered by how strongly the researchers Chence Shi 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (2018) — 1,089 citations
• GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (2022) — 734 citations
• Learning Gradient Fields for Molecular Conformation Generation (2021) — 261 citations
• GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation (2020) — 553 citations
• TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery (2022) — 88 citations
• Predicting Molecular Conformation via Dynamic Graph Score Matching (2021) — 120 citations