Stephan Eismann

Molecular Biology · 2020–2021

A researcher in the Science corpus: 6 papers; trusts 22 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 133 citations into it; #11,832 of 17,773 by trust here, #7,496 by citations
• Materials Chemistry — 82 citations into it; #7,385 of 11,513 by trust here, #6,189 by citations
• Computational Theory and Mathematics — 56 citations into it; #11,064 of 12,182 by trust here, #2,761 by citations
• Computational Biology & Bioinformatics — 43 citations into it; #550 of 660 by trust here, #484 by citations
• Computational Chemistry & Drug Discovery — 32 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Model Architectures — 17 citations into it
• Artificial Intelligence — 16 citations into it

Whom Stephan Eismann trusts overall

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

1. S. Parasuraman
2. Risi Kondor
3. Nathaniel Thomas
4. Taco Cohen
5. Brandon M. Anderson
6. Alexander Sergeev
7. Max Welling
8. Thom Vreven
9. Rong Chen
10. Guillaume Pagès

Who trusts Stephan Eismann overall

1. Octavian-Eugen Ganea
2. Hannes Stärk
3. Bowen Jing
4. Zhuoran Qiao
5. Chloe Hsu
6. Minkyung Baek
7. Raphael J. L. Townshend
8. Shuo Zhang
9. Kevin Kaichuang Yang
10. Zhangyang Gao

Papers from Stephan Eismann's perspective

Ordered by how strongly the researchers Stephan Eismann 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. 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. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. Et al
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
• Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes (2020) — 68 citations
• Geometric Prediction: Moving Beyond Scalars (2020) — 14 citations
• Equivariant Graph Neural Networks for 3D Macromolecular Structure (2021) — 129 citations
• ATOM3D: Tasks On Molecules in Three Dimensions (2020) — 173 citations