Alexander Rives

Molecular Biology · 2003–2025

A researcher in the Science corpus: 10 papers; trusts 67 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 363 citations into it; #2,283 of 17,773 by trust here, #1,690 by citations
• Computational Biology & Bioinformatics — 268 citations into it; #444 of 660 by trust here, #292 by citations
• Materials Chemistry — 82 citations into it
• Artificial Intelligence — 67 citations into it
• Representation & Self-Supervised Learning — 49 citations into it; #2,425 of 3,145 by trust here, #790 by citations
• Genetics — 48 citations into it
• Natural Language Processing — 43 citations into it
• Model Architectures — 34 citations into it

Whom Alexander Rives trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Johannes Söding
4. Ilya Sutskever
5. Alec Radford
6. Diederik P. Kingma
7. Jinbo Xu
8. Alex Krizhevsky
9. Yann LeCun
10. Kaiming He

Who trusts Alexander Rives overall

1. Réka Albert
2. Michael Heinzinger
3. Kevin Kaichuang Yang
4. Nadav Brandes
5. Roded Sharan
6. Ahmed Elnaggar
7. Brian L. Hie
8. Ratul Chowdhury
9. Konstantin Weissenow
10. Christian Dallago

Papers from Alexander Rives's perspective

Ordered by how strongly the researchers Alexander Rives 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019) — 3,265 citations
• MSA Transformer (2021) — 735 citations
• Learning inverse folding from millions of predicted structures (2022) — 618 citations
• Modular organization of cellular networks (2003) — 729 citations
• Evolutionary-scale prediction of atomic level protein structure with a language model (2022) — 5,058 citations
• Energy-based models for atomic-resolution protein conformations (2020) — 61 citations
• Language models enable zero-shot prediction of the effects of mutations on protein function (2021) — 898 citations
• Transformer protein language models are unsupervised structure learners (2020) — 394 citations
• Simulating 500 million years of evolution with a language model. (2025) — 309 citations
• Simulating 500 million years of evolution with a language model (2024) — 677 citations