Hervé Le Borgne

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 563 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #9,024 of 21,575 by trust here, #11,083 by citations
• Artificial Intelligence — 66 citations into it; #29,554 of 35,345 by trust here, #30,347 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Generative Models — 20 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Model Architectures — 15 citations into it
• Media Technology — 10 citations into it
• Optimization & Training — 9 citations into it

Whom Hervé Le Borgne trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Karen Simonyan
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Diederik P. Kingma
9. Andrew Zisserman
10. Ross B. Girshick

Who trusts Hervé Le Borgne overall

1. Eden Belouadah
2. Andrey Voynov
3. Daiqing Li
4. Nurit Spingarn-Eliezer
5. Daniel Roich
6. Adrian Daniel Popescu
7. Huan Ling

Papers from Hervé Le Borgne's perspective

Ordered by how strongly the researchers Hervé Le Borgne 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Controlling generative models with continuous factors of variations (2020) — 133 citations
• Learning More Universal Representations for Transfer-Learning (2017) — 60 citations
• Harnessing noisy Web images for deep representation (2017) — 13 citations