Ali Madani

Molecular Biology · 2017–2023

A researcher in the Science corpus: 12 papers; trusts 99 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 194 citations into it
• Artificial Intelligence — 172 citations into it
• Computational Biology & Bioinformatics — 136 citations into it
• Computer Vision and Pattern Recognition — 81 citations into it
• Natural Language Processing — 72 citations into it
• Representation & Self-Supervised Learning — 48 citations into it
• Materials Chemistry — 43 citations into it
• Large Language Models — 41 citations into it

Whom Ali Madani trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Ilya Sutskever
5. Diederik P. Kingma
6. Kaiming He
7. Tomas Mikolov
8. Richard Socher
9. Yann LeCun
10. Alec Radford

Who trusts Ali Madani overall

1. Krzysztof Marcin Choromanski
2. Jeffrey A. Ruffolo
3. Ratul Chowdhury
4. Michael Jendrusch
5. Maithra Raghu
6. Erik Nijkamp
7. Konstantin Weissenow
8. Roshan Rao
9. Amy X. Lu
10. Pascal Sturmfels

Papers from Ali Madani's perspective

Ordered by how strongly the researchers Ali Madani 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep learning-enabled medical computer vision (2021) — 1,179 citations
• ProGen: Language Modeling for Protein Generation (2020) — 353 citations
• BERTology Meets Biology: Interpreting Attention in Protein Language Models (2020) — 381 citations
• Deep neural language modeling enables functional protein generation across families (2021) — 55 citations
• ProGen2: Exploring the Boundaries of Protein Language Models (2022) — 555 citations
• FLIP: Benchmark tasks in fitness landscape inference for proteins (2021) — 190 citations
• Large language models generate functional protein sequences across diverse families (2023) — 1,084 citations
• Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models (2020) — 27 citations
• Chest x-ray generation and data augmentation for cardiovascular abnormality classification (2018) — 168 citations
• Fast and accurate view classification of echocardiograms using deep learning (2018) — 503 citations
• Bimodal Network Architectures for Automatic Generation of Image Annotation from Text (2018) — 20 citations
• Fast and accurate classification of echocardiograms using deep learning (2017) — 24 citations