Jean Feydy

Computer Vision and Pattern Recognition · 2018–2020

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

Subfields

• Computer Vision and Pattern Recognition — 52 citations into it
• Artificial Intelligence — 36 citations into it; #33,994 of 35,345 by trust here, #26,682 by citations
• Computational Mechanics — 29 citations into it; #8,769 of 10,001 by trust here, #6,582 by citations
• Model Architectures — 16 citations into it
• Graph Learning — 14 citations into it
• Molecular Biology — 14 citations into it
• ML Systems & Hardware — 12 citations into it
• Computational Theory and Mathematics — 10 citations into it; #11,777 of 12,182 by trust here, #7,501 by citations

Whom Jean Feydy trusts overall

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

1. G. Peyr'e
2. Hubert Nguyen
3. Marco Cuturi
4. Sergey Ioffe
5. Geoffrey E. Hinton
6. Filippo Santambrogio
7. Christian Léonard
8. Christian Szegedy
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Jean Feydy overall

1. Freyr Sverrisson
2. Bruno E. Correia
3. G. Peyr'e
4. Pierre Ablin
5. Michael Arbel
6. Pierre L. Dognin
7. Pinaki Nath Chowdhury
8. Ankit Gupta
9. Marco Cuturi
10. Octavian-Eugen Ganea

Papers from Jean Feydy's perspective

Ordered by how strongly the researchers Jean Feydy 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Fast end-to-end learning on protein surfaces (2020) — 197 citations
• Interpolating between Optimal Transport and MMD using Sinkhorn Divergences (2018) — 706 citations
• Kernel Operations on the GPU, with Autodiff, without Memory Overflows (2020) — 222 citations
• Fast geometric learning with symbolic matrices (2020) — 49 citations