Stéphane Lafon

Computer Vision and Pattern Recognition · 2005–2010

A researcher in the Science corpus: 9 papers; trusts 130 researchers and is trusted by 125 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 36 citations into it; #4,764 of 21,575 by trust here, #7,176 by citations
• Computational Theory and Mathematics — 28 citations into it; #971 of 12,182 by trust here, #354 by citations
• Representation & Self-Supervised Learning — 26 citations into it; #273 of 3,145 by trust here, #645 by citations
• Artificial Intelligence — 25 citations into it; #7,348 of 35,345 by trust here, #9,557 by citations
• Computational Mechanics — 14 citations into it
• Statistical Learning Theory — 9 citations into it
• Statistical and Nonlinear Physics — 8 citations into it; #9,341 of 10,127 by trust here, #3,100 by citations
• Molecular Biology — 5 citations into it

Whom Stéphane Lafon trusts overall

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

1. Ronald R. Coifman
2. Jianbo Shi
3. Mikhail Belkin
4. Sam T. Roweis
5. Partha Niyogi
6. Yoshua Bengio
7. Vladimir Naumovich Vapnik
8. Marina Meila
9. Jitendra Malik
10. Vladimir Naumovich Vapni

Who trusts Stéphane Lafon overall

1. Facundo M'emoli
2. Ronald R. Coifman
3. Amit Singer
4. David I. Shuman
5. Michael M. Bronstein
6. Uri Shaham
7. Peng Zhang
8. Philipp Metzner
9. A. Bronstein
10. Miloš Radovanović

Papers from Stéphane Lafon's perspective

Ordered by how strongly the researchers Stéphane Lafon 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Google Street View: Capturing the World at Street Level (2010) — 756 citations
• Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization (2006) — 688 citations
• Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators (2005) — 509 citations
• Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. (2005) — 1,850 citations
• Diffusion maps, spectral clustering and reaction coordinates of dynamical systems (2005) — 1,078 citations
• Diffusion Maps, Reduction Coordinates, and Low Dimensional Representation of Stochastic Systems (2008) — 315 citations
• Geometric harmonics: A novel tool for multiscale out-of-sample extension of empirical functions (2006) — 262 citations
• Diffusion maps (2006)
• Data Fusion and Multicue Data Matching by Diffusion Maps (2006) — 341 citations