John Ingraham

Molecular Biology · 2015–2022

A researcher in the Science corpus: 9 papers; trusts 55 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 284 citations into it; #2,942 of 17,773 by trust here, #3,835 by citations
• Computational Biology & Bioinformatics — 139 citations into it; #148 of 660 by trust here, #210 by citations
• Artificial Intelligence — 50 citations into it
• Materials Chemistry — 49 citations into it
• Probabilistic & Bayesian Methods — 38 citations into it
• Generative Models — 35 citations into it; #552 of 1,956 by trust here, #540 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Statistical and Nonlinear Physics — 21 citations into it

Whom John Ingraham trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Radford M. Neal
4. David John Cameron MacKay
5. Michael I. Jordan
6. Alex Krizhevsky
7. Yoshua Bengio
8. Danilo Jimenez Rezende
9. Ilya Sutskever
10. Max Welling

Who trusts John Ingraham overall

1. Soumya Ghosh
2. Wengong Jin
3. Federico Baldassarre
4. Kevin Kaichuang Yang
5. Erik Nijkamp
6. Joshua Meier
7. Roshan Rao
8. Sam Sinai
9. Adam J. Riesselman
10. Christos Louizos

Papers from John Ingraham's perspective

Ordered by how strongly the researchers John Ingraham 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Structured states of disordered proteins from genomic sequences (2016) — 132 citations
• Mutation effects predicted from sequence co-variation (2015) — 766 citations
• Deep generative models of genetic variation capture the effects of mutations (2018) — 627 citations
• The EVcouplings Python framework for coevolutionary sequence analysis (2018) — 276 citations
• 3D RNA and Functional Interactions from Evolutionary Couplings. (2015) — 166 citations
• Illuminating protein space with a programmable generative model (2022) — 605 citations
• Generative Models for Graph-Based Protein Design (2019) — 631 citations
• Learning Protein Structure with a Differentiable Simulator (2018) — 161 citations
• Variational Inference for Sparse and Undirected Models (2016) — 35 citations