Raphael R. Eguchi

Molecular Biology · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 44 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 150 citations into it; #9,432 of 17,773 by trust here, #6,535 by citations
• Computational Biology & Bioinformatics — 76 citations into it
• Materials Chemistry — 69 citations into it
• Artificial Intelligence — 30 citations into it
• Computational Chemistry & Drug Discovery — 27 citations into it
• Spectroscopy — 25 citations into it
• Generative Models — 24 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it

Whom Raphael R. Eguchi trusts overall

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

1. David Baker
2. Po-Ssu Huang
3. Alec Radford
4. S. Parasuraman
5. Brian Kuhlman
6. Sergey Ioffe
7. Namrata Anand
8. Diederik P. Kingma
9. Michael D. Tyka
10. Colin A. Smith

Who trusts Raphael R. Eguchi overall

1. Xingjie Pan
2. Michael Jendrusch
3. Namrata Anand
4. Robin Pearce
5. Wenhao Gao
6. Jue Wang
7. Casper A. Goverde

Papers from Raphael R. Eguchi's perspective

Ordered by how strongly the researchers Raphael R. Eguchi 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. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet Large Scale Visual Recognition Challenge
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet: A large-scale hierarchical image database

Their papers

• Protein sequence design with a learned potential (2020) — 135 citations
• ProGen: Language Modeling for Protein Generation (2020) — 353 citations
• Protein sequence design with a learned potential (2022) — 69 citations
• Fully differentiable full-atom protein backbone generation (2019) — 55 citations