Adam J. Riesselman

Molecular Biology · 2015–2021

A researcher in the Science corpus: 6 papers; trusts 24 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 224 citations into it; #3,336 of 17,773 by trust here, #4,543 by citations
• Computational Biology & Bioinformatics — 98 citations into it; #226 of 660 by trust here, #362 by citations
• Radiology, Nuclear Medicine and Imaging — 49 citations into it
• Artificial Intelligence — 36 citations into it
• Immunology — 36 citations into it
• Natural Language Processing — 21 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Materials Chemistry — 17 citations into it

Whom Adam J. Riesselman trusts overall

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

1. Geoffrey E. Hinton
2. Ilya Sutskever
3. Diederik P. Kingma
4. Yoshua Bengio
5. Alex Graves
6. Jimmy Ba
7. Yann LeCun
8. Alex Krizhevsky
9. Debora Marks
10. Sepp Hochreiter

Who trusts Adam J. Riesselman overall

1. Roshan Rao
2. Joshua Meier
3. Kevin Kaichuang Yang
4. Zak Costello
5. Yunan Luo
6. Ali Madani
7. Erik Nijkamp
8. Brian L. Hie
9. Jung-Eun Shin
10. Christian Dallago

Papers from Adam J. Riesselman's perspective

Ordered by how strongly the researchers Adam J. Riesselman trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• 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
• Protein design and variant prediction using autoregressive generative models (2021) — 363 citations
• 3D RNA and Functional Interactions from Evolutionary Couplings. (2015) — 166 citations
• Accelerating Protein Design Using Autoregressive Generative Models (2019) — 45 citations
• Learning Protein Structure with a Differentiable Simulator (2018) — 161 citations