Patricia Suriana

Molecular Biology · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 28 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 95 citations into it; #14,694 of 17,773 by trust here, #8,905 by citations
• Materials Chemistry — 62 citations into it; #9,197 of 11,513 by trust here, #7,154 by citations
• Computational Theory and Mathematics — 51 citations into it; #11,005 of 12,182 by trust here, #2,565 by citations
• Computational Biology & Bioinformatics — 35 citations into it
• Computational Chemistry & Drug Discovery — 26 citations into it
• Graph Learning — 17 citations into it
• Artificial Intelligence — 13 citations into it
• AI for Physical Sciences — 8 citations into it

Whom Patricia Suriana trusts overall

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

1. Justin Gilmer
2. Wen Torng
3. Guillaume Pagès
4. S. Parasuraman
5. Georgy Derevyanko
6. Karolis Uziela
7. Sergei Grudinin
8. Renzhi Cao
9. Yoshua Bengio
10. Yang Zhang

Who trusts Patricia Suriana overall

1. Vignesh Ram Somnath
2. Hannes Stärk
3. Zhuoran Qiao
4. Kevin Kaichuang Yang

Papers from Patricia Suriana's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. Et al
10. Going deeper with convolutions

Their papers

• 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
• ATOM3D: Tasks On Molecules in Three Dimensions (2020) — 173 citations
• Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code (2018) — 314 citations