Nadav Brandes

Molecular Biology · 2021–2023

A researcher in the Science corpus: 4 papers; trusts 45 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 95 citations into it; #7,519 of 17,773 by trust here, #13,284 by citations
• Artificial Intelligence — 55 citations into it; #28,049 of 35,345 by trust here, #32,362 by citations
• Computational Biology & Bioinformatics — 55 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Natural Language Processing — 30 citations into it
• Large Language Models — 26 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Biophysics — 6 citations into it

Whom Nadav Brandes trusts overall

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

1. Dan Ofer
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Jacob Devlin
6. Tomas Mikolov
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Yann LeCun
10. Richard Socher

Who trusts Nadav Brandes overall

1. Max Schubach
2. Grant Goldman
3. Konstantin Weissenow
4. Charlotte H. Wang

Papers from Nadav Brandes's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Long Short-Term Memory
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• The language of proteins: NLP, machine learning & protein sequences (2021) — 336 citations
• ProteinBERT: a universal deep-learning model of protein sequence and function (2021) — 979 citations
• Genome-wide prediction of disease variant effects with a deep protein language model (2023) — 348 citations
• Genome-wide prediction of disease variants with a deep protein language model (2022) — 19 citations