Raphael J. L. Townshend

Molecular Biology · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 37 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 140 citations into it; #5,333 of 17,773 by trust here, #6,685 by citations
• Materials Chemistry — 87 citations into it; #3,123 of 11,513 by trust here, #5,658 by citations
• Computational Theory and Mathematics — 63 citations into it; #5,959 of 12,182 by trust here, #2,161 by citations
• Computational Biology & Bioinformatics — 44 citations into it; #548 of 660 by trust here, #484 by citations
• Computational Chemistry & Drug Discovery — 37 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it
• Graph Learning — 18 citations into it
• Model Architectures — 18 citations into it

Whom Raphael J. L. Townshend trusts overall

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

1. S. Parasuraman
2. Wen Torng
3. Risi Kondor
4. Nathaniel Thomas
5. Justin Gilmer
6. Geoffrey E. Hinton
7. Yang Zhang
8. J. Behler
9. Yoshua Bengio
10. Max Welling

Who trusts Raphael J. L. Townshend overall

1. Vignesh Ram Somnath
2. Hannes Stärk
3. Minkyung Baek
4. Octavian-Eugen Ganea
5. Charlotte Bunne
6. Bowen Jing
7. Yi Liao
8. Simon L. Batzner
9. Kevin Kaichuang Yang
10. Andreas Krause

Papers from Raphael J. L. Townshend's perspective

Ordered by how strongly the researchers Raphael J. L. Townshend 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Geometric deep learning of RNA structure (2021) — 339 citations
• Learning from Protein Structure with Geometric Vector Perceptrons (2020) — 664 citations
• End-to-End Learning on 3D Protein Structure for Interface Prediction (2018) — 118 citations
• Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes (2020) — 68 citations
• Geometric Prediction: Moving Beyond Scalars (2020) — 14 citations
• ATOM3D: Tasks On Molecules in Three Dimensions (2020) — 173 citations