Kevin Kaichuang Yang

Molecular Biology · 2017–2023

A researcher in the Science corpus: 13 papers; trusts 176 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 292 citations into it; #4,999 of 17,773 by trust here, #5,504 by citations
• Computational Biology & Bioinformatics — 131 citations into it; #340 of 660 by trust here, #386 by citations
• Artificial Intelligence — 79 citations into it; #23,012 of 35,345 by trust here, #13,353 by citations
• Materials Chemistry — 53 citations into it
• Generative Models — 45 citations into it
• Representation & Self-Supervised Learning — 45 citations into it; #2,707 of 3,145 by trust here, #2,066 by citations
• Computer Vision and Pattern Recognition — 43 citations into it
• Genetics — 35 citations into it

Whom Kevin Kaichuang Yang trusts overall

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

1. Philip A. Romero
2. Claire N. Bedbrook
3. Geoffrey E. Hinton
4. Frances H. Arnold
5. Diederik P. Kingma
6. Yoshua Bengio
7. Andreas Krause
8. Jerome H. Friedman
9. Kevin P. Murphy
10. Corinna Cortes

Who trusts Kevin Kaichuang Yang overall

1. Zachary Wu
2. Yunan Luo
3. Frances H. Arnold
4. Tristan Bepler
5. Stanislav Mazurenko
6. Dan Ofer
7. Zuobai Zhang
8. Brian L. Hie
9. Chloe Hsu
10. Xinqiang Ding

Papers from Kevin Kaichuang Yang's perspective

Ordered by how strongly the researchers Kevin Kaichuang Yang 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learned protein embeddings for machine learning (2018) — 132 citations
• Machine-learning-guided directed evolution for protein engineering (2018) — 993 citations
• Protein sequence design with deep generative models (2021) — 165 citations
• Learned protein embeddings for machine learning (2018) — 195 citations
• Evolutionary velocity with protein language models (2021) — 151 citations
• FLIP: Benchmark tasks in fitness landscape inference for proteins (2021) — 190 citations
• Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization (2017) — 112 citations
• Exploring evolution-based & -free protein language models as protein function predictors (2022) — 16 citations
• Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023) — 129 citations
• Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics (2019) — 181 citations
• Protein structure generation via folding diffusion (2022) — 297 citations
• Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design (2019) — 18 citations
• Machine learning in protein engineering (2018) — 33 citations