Roshan Rao

Molecular Biology · 2018–2022

A researcher in the Science corpus: 8 papers; trusts 310 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 240 citations into it; #5,789 of 17,773 by trust here, #3,204 by citations
• Computational Biology & Bioinformatics — 187 citations into it; #365 of 660 by trust here, #229 by citations
• Artificial Intelligence — 74 citations into it
• Computer Vision and Pattern Recognition — 65 citations into it; #17,952 of 21,575 by trust here, #16,787 by citations
• Materials Chemistry — 51 citations into it
• Representation & Self-Supervised Learning — 51 citations into it; #2,267 of 3,145 by trust here, #1,103 by citations
• Natural Language Processing — 28 citations into it
• Large Language Models — 27 citations into it

Whom Roshan Rao trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. L. V. D. van der Maaten
6. Yann LeCun
7. Ilya Sutskever
8. Diederik P. Kingma
9. Badri Adhikari
10. Karen Simonyan

Who trusts Roshan Rao overall

1. Ahmed Elnaggar
2. Michael Heinzinger
3. Kevin Kaichuang Yang
4. Joshua Meier
5. Jin Su
6. Konstantin Weissenow
7. Brian L. Hie
8. Nadav Brandes
9. Ali Madani
10. Christian Dallago

Papers from Roshan Rao's perspective

Ordered by how strongly the researchers Roshan Rao 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Evaluating Protein Transfer Learning with TAPE (2019) — 1,047 citations
• MSA Transformer (2021) — 735 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
• Transformer protein language models are unsupervised structure learners (2020) — 394 citations
• Single Layers of Attention Suffice to Predict Protein Contacts (2020) — 35 citations
• GPU accelerated t-distributed stochastic neighbor embedding (2019) — 79 citations
• T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data (2018) — 106 citations