Jean-François Paiement

Artificial Intelligence · 2003–2014

A researcher in the Science corpus: 3 papers; trusts 23 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #29,197 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #17,943 of 21,575 by trust here, #10,677 by citations
• Optimization & Training — 12 citations into it
• Computational Mechanics — 10 citations into it; #8,279 of 10,001 by trust here, #2,857 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Statistics and Probability — 6 citations into it

Whom Jean-François Paiement trusts overall

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

1. Andrew Y. Ng
2. Shai Shalev-Shwartz
3. Sam T. Roweis
4. Lawrence K. Saul
5. Vin de Silva
6. Christopher K. I. Williams
7. Josh Tenenbaum
8. L. Eon Bottou
9. Martin A. Zinkevich
10. Peter Richtárik

Who trusts Jean-François Paiement overall

1. Yoshua Bengio
2. L. V. D. van der Maaten
3. Feiping Nie
4. Ronald R. Coifman
5. Petros Drineas
6. Yi Yang
7. Jiaxin Shi
8. Stéphane Lafon
9. C. Burges
10. Kilian Q. Weinberger

Papers from Jean-François Paiement's perspective

Ordered by how strongly the researchers Jean-François Paiement 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. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Learning Eigenfunctions Links Spectral Embedding and Kernel PCA (2004) — 342 citations
• Accelerated Parallel Optimization Methods for Large Scale Machine Learning (2014) — 7 citations
• Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering (2003) — 1,157 citations