Dan Ofer

Molecular Biology · 2015–2021

A researcher in the Science corpus: 3 papers; trusts 56 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 83 citations into it; #2,700 of 17,773 by trust here, #11,410 by citations
• Artificial Intelligence — 50 citations into it; #27,934 of 35,345 by trust here, #32,362 by citations
• Computational Biology & Bioinformatics — 39 citations into it
• Natural Language Processing — 28 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Large Language Models — 21 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Spectroscopy — 13 citations into it

Whom Dan Ofer trusts overall

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

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

Who trusts Dan Ofer overall

1. Nadav Brandes
2. Kevin Kaichuang Yang
3. Tristan Bepler
4. Domenico Cozzetto
5. Konstantin Weissenow
6. Ronghui You
7. Zachary Wu
8. Bonnie Berger
9. Grant Goldman
10. Federico Minneci

Papers from Dan Ofer's perspective

Ordered by how strongly the researchers Dan Ofer 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• ProFET: Feature engineering captures high-level protein functions (2015) — 104 citations
• 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