Jason Yim

Molecular Biology · 2021–2023

A researcher in the Science corpus: 4 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 80 citations into it
• Generative Models — 36 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Artificial Intelligence — 23 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Statistical and Nonlinear Physics — 10 citations into it

Whom Jason Yim trusts overall

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

1. Diederik P. Kingma
2. Jonathan Ho
3. Geoffrey E. Hinton
4. Jimmy Ba
5. B. Hall
6. Yang Song
7. Yoshua Bengio
8. Alex Krizhevsky
9. Yann LeCun
10. Jascha Narain Sohl-Dickstein

Who trusts Jason Yim overall

1. Yeqing Lin
2. Mohammed Alquraishi
3. Luhuan Wu

Papers from Jason Yim's perspective

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

Their papers

• Protein complex prediction with AlphaFold-Multimer (2021) — 3,230 citations
• Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022) — 200 citations
• Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022) — 347 citations
• SE(3) diffusion model with application to protein backbone generation (2023) — 343 citations