Bharath Ramsundar

Computational Theory and Mathematics · 2015–2023

A researcher in the Science corpus: 13 papers; trusts 26 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 179 citations into it; #1,325 of 12,182 by trust here, #128 by citations
• Molecular Biology — 168 citations into it; #2,299 of 17,773 by trust here, #1,899 by citations
• Artificial Intelligence — 158 citations into it; #33,766 of 35,345 by trust here, #7,413 by citations
• Materials Chemistry — 139 citations into it; #904 of 11,513 by trust here, #1,486 by citations
• Computational Chemistry & Drug Discovery — 125 citations into it; #90 of 425 by trust here, #43 by citations
• Computer Vision and Pattern Recognition — 79 citations into it
• Natural Language Processing — 33 citations into it
• Graph Learning — 27 citations into it; #621 of 693 by trust here, #297 by citations

Whom Bharath Ramsundar trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Brian K. Shoichet
4. Yann LeCun
5. Ronan Collobert
6. Alex Krizhevsky
7. Ilya Sutskever
8. Oriol Vinyals
9. Diederik P. Kingma
10. Christian Szegedy

Who trusts Bharath Ramsundar overall

1. Garrett B. Goh
2. Chaopeng Shen
3. Kyle Swanson
4. Charles Siegel
5. Junying Li
6. Dougal Maclaurin
7. Steven M. Kearnes
8. Dan Busbridge
9. Mikhail Zaslavskiy
10. Abhinav Vishnu

Papers from Bharath Ramsundar's perspective

Ordered by how strongly the researchers Bharath Ramsundar 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• A guide to deep learning in healthcare (2019) — 4,405 citations
• Is Multitask Deep Learning Practical for Pharma? (2017) — 241 citations
• MoleculeNet: a benchmark for molecular machine learning (2017) — 2,566 citations
• Massively Multitask Networks for Drug Discovery (2015) — 499 citations
• Low Data Drug Discovery with One-Shot Learning (2016) — 773 citations
• Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models (2017) — 486 citations
• Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches (2016) — 303 citations
• PotentialNet for Molecular Property Prediction (2018) — 428 citations
• Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity (2017) — 208 citations
• ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction (2020) — 752 citations
• Scientific discovery in the age of artificial intelligence (2023) — 1,745 citations
• ChemBERTa-2: Towards Chemical Foundation Models (2022) — 312 citations
• enchmark for molecular machine learning † (2017)