Victor Garcia Satorras

Molecular Biology · 2017–2025

A researcher in the Science corpus: 5 papers; trusts 180 researchers and is trusted by 114 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 84 citations into it; #2,917 of 17,773 by trust here, #8,480 by citations
• Materials Chemistry — 68 citations into it; #1,190 of 11,513 by trust here, #5,658 by citations
• Artificial Intelligence — 64 citations into it; #2,595 of 35,345 by trust here, #5,153 by citations
• Computer Vision and Pattern Recognition — 53 citations into it
• Computational Theory and Mathematics — 45 citations into it; #10,676 of 12,182 by trust here, #3,796 by citations
• Generative Models — 39 citations into it
• Graph Learning — 33 citations into it; #67 of 693 by trust here, #139 by citations
• Computational Chemistry & Drug Discovery — 29 citations into it

Whom Victor Garcia Satorras trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Max Welling
4. Diederik P. Kingma
5. Oriol Vinyals
6. Thomas Kipf
7. Yoshua Bengio
8. Ilya Sutskever
9. Yann LeCun
10. Ruslan Salakhutdinov

Who trusts Victor Garcia Satorras overall

1. Han-Jia Ye
2. Wenbin Li
3. Eli Schwartz
4. Spyros Gidaris
5. Chi Zhang
6. Zhuoran Qiao
7. Xu Han
8. Debasmit Das
9. Hoang NT
10. Timothy M. Hospedales

Papers from Victor Garcia Satorras's perspective

Ordered by how strongly the researchers Victor Garcia Satorras 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Few-Shot Learning with Graph Neural Networks (2017) — 1,368 citations
• E(n) Equivariant Graph Neural Networks (2021) — 1,567 citations
• Equivariant Diffusion for Molecule Generation in 3D (2022) — 962 citations
• Scalable emulation of protein equilibrium ensembles with generative deep learning (2025) — 335 citations
• Equivariant 3D-conditional diffusion model for molecular linker design (2022) — 284 citations