Gabriele Corso

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 72 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it
• Molecular Biology — 48 citations into it
• Computational Theory and Mathematics — 37 citations into it; #12,181 of 12,182 by trust here, #2,761 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #18,972 of 21,575 by trust here, #19,310 by citations
• Generative Models — 32 citations into it; #1,578 of 1,956 by trust here, #1,134 by citations
• Materials Chemistry — 31 citations into it
• Graph Learning — 23 citations into it
• Model Architectures — 18 citations into it

Whom Gabriele Corso trusts overall

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

1. Diederik P. Kingma
2. Yang Song
3. Alex Graves
4. Geoffrey E. Hinton
5. Kaiming He
6. Yoshua Bengio
7. Radford M. Neal
8. Alex Krizhevsky
9. Yann LeCun
10. Oriol Vinyals

Who trusts Gabriele Corso overall

1. Vijay Prakash Dwivedi
2. Yuanqi Du
3. Shuya Nakata
4. Dexiong Chen
5. Siqi Miao
6. Shengchao Liu
7. Giorgos Bouritsas
8. Zhaocheng Zhu
9. Yuyang Wang
10. Devin Kreuzer

Papers from Gabriele Corso's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Subspace Diffusion Generative Models (2022) — 92 citations
• DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022) — 790 citations
• Principal Neighbourhood Aggregation for Graph Nets (2020) — 873 citations
• Torsional Diffusion for Molecular Conformer Generation (2022) — 391 citations