Ilyes Khemakhem

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 21 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #14,185 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #9,235 of 21,575 by trust here, #13,815 by citations
• Generative Models — 12 citations into it; #872 of 1,956 by trust here, #1,485 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it; #851 of 1,525 by trust here, #978 by citations
• Signal Processing — 6 citations into it

Whom Ilyes Khemakhem trusts overall

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

1. Diederik P. Kingma
2. Aapo Hyvärinen
3. Yoshua Bengio
4. Danilo Jimenez Rezende
5. Geoffrey E. Hinton
6. Adi Ben-Israel
7. Max Welling
8. Jan Peters
9. Jimmy Ba
10. Arthur Gretton

Who trusts Ilyes Khemakhem overall

1. Peter Sorrenson
2. Francesco Locatello
3. Hermanni Hälvä
4. Geoffrey Roeder
5. Mengyue Yang
6. Sébastien Lachapelle
7. Yash Sharma
8. David A. Klindt
9. Andrea Dittadi
10. Aapo Hyvärinen

Papers from Ilyes Khemakhem's perspective

Ordered by how strongly the researchers Ilyes Khemakhem 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Variational Autoencoders and Nonlinear ICA: A Unifying Framework (2019) — 785 citations