Sébastien Bougleux

Computer Vision and Pattern Recognition · 2008

A researcher in the Science corpus: 2 papers; trusts 45 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #5,696 of 21,575 by trust here, #9,563 by citations
• Computational Mechanics — 20 citations into it; #1,716 of 10,001 by trust here, #2,644 by citations
• Mathematical Physics — 9 citations into it; #2,841 of 8,562 by trust here, #2,795 by citations
• Media Technology — 9 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Artificial Intelligence — 7 citations into it
• Computer Graphics and Computer-Aided Design — 7 citations into it
• Geometry and Topology — 6 citations into it

Whom Sébastien Bougleux trusts overall

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

1. Stéphane Mallat
2. Stanley J. Osher
3. Guy Gilboa
4. David L. Donoho
5. Gabriel Taubin
6. Yurii E. Nesterov
7. Antonin Chambolle
8. A. Buades
9. Paul Tseng
10. Tony F. Chan

Who trusts Sébastien Bougleux overall

1. Xiaoqun Zhang
2. David K. Hammond
3. He He
4. Chao Zeng
5. Charles-Alban Deledalle
6. François Rousseau
7. Daniel Glasner
8. Jun Xu
9. G. Yu
10. Luca Calatroni

Papers from Sébastien Bougleux's perspective

Ordered by how strongly the researchers Sébastien Bougleux 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Normalized cuts and image segmentation
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. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Non-local Regularization of Inverse Problems (2008) — 383 citations
• Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing (2008) — 506 citations