Silvere Bonnabel

Artificial Intelligence · 2010–2019

A researcher in the Science corpus: 4 papers; trusts 46 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #3,756 of 35,345 by trust here, #16,471 by citations
• Computer Vision and Pattern Recognition — 14 citations into it
• Aerospace Engineering — 12 citations into it; #3,943 of 6,421 by trust here, #5,830 by citations
• Optimization & Training — 10 citations into it
• Computational Mechanics — 8 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Applied Mathematics — 5 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Silvere Bonnabel trusts overall

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

1. Shun-ichi Amari
2. Mark J van Rossum
3. Daniel D. Lee
4. Gilles Meyer
5. Stephen P. Boyd
6. Kilian Q. Weinberger
7. Francis R. Bach
8. Geoffrey E. Hinton
9. Luc Moreau
10. Tamer Basar

Who trusts Silvere Bonnabel overall

1. Zhiwu Huang
2. Octavian-Eugen Ganea
3. Gary Bécigneul
4. Maximilian Nickel
5. Ines Chami
6. Douwe Kiela
7. Luc Van Gool
8. Hongyi Zhang
9. Ivana Balazevic
10. Arslan Chaudhry

Papers from Silvere Bonnabel's perspective

Ordered by how strongly the researchers Silvere Bonnabel 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Stochastic Gradient Descent on Riemannian Manifolds (2011) — 716 citations
• AI-IMU Dead-Reckoning (2019) — 299 citations
• The Invariant Extended Kalman Filter as a Stable Observer (2014) — 667 citations
• Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach (2010) — 93 citations