Robert Verkuil
Molecular Biology · 2021–2025
A researcher in the Science corpus: 6 papers; trusts 9 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Molecular Biology — 220 citations into it; #17,441 of 17,773 by trust here, #5,084 by citations
- Computational Biology & Bioinformatics — 147 citations into it
- Computational Theory and Mathematics — 16 citations into it
- Natural Language Processing — 16 citations into it
- Spectroscopy — 12 citations into it
- Generative Models — 11 citations into it
Whom Robert Verkuil trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Adam J. Riesselman
- S. Parasuraman
- John Ingraham
- Roshan Rao
- Jianyi Yang
- Bowen Jing
- Geoffrey E. Hinton
- Kevin Kaichuang Yang
- Baris E. Suzek
- Yoshua Bengio
Who trusts Robert Verkuil overall
Papers from Robert Verkuil's perspective
Ordered by how strongly the researchers Robert Verkuil trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- MSA Transformer (2021) — 735 citations
- Learning inverse folding from millions of predicted structures (2022) — 618 citations
- Evolutionary-scale prediction of atomic level protein structure with a language model (2022) — 5,058 citations
- Language models enable zero-shot prediction of the effects of mutations on protein function (2021) — 898 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