Xavier Muller

Representation & Self-Supervised Learning · 2011

A researcher in the Science corpus: 5 papers; trusts 44 researchers and is trusted by 70 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 41 citations into it; #435 of 3,145 by trust here, #287 by citations
• Artificial Intelligence — 37 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it; #13,313 of 21,575 by trust here, #10,010 by citations
• Model Architectures — 14 citations into it
• Generative Models — 13 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Optimization & Training — 9 citations into it

Whom Xavier Muller trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Pascal Vincent
5. Yann LeCun
6. Ruslan Salakhutdinov
7. Hugo Larochelle
8. Marc'Aurelio Ranzato
9. Koray Kavukcuoglu
10. Diederik P. Kingma

Who trusts Xavier Muller overall

1. Yoshua Bengio
2. Kyunghyun Cho
3. Aaron C. Courville
4. Sergey Levine
5. Ruslan Salakhutdinov
6. Quoc V. Le
7. Trevor Darrell
8. Dacheng Tao
9. Zhe Gan
10. Mohit Bansal

Papers from Xavier Muller's perspective

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

Their papers

• Higher Order Contractive Auto-Encoder (2011) — 283 citations
• Contractive Auto-Encoders: Explicit Invariance During Feature Extraction (2011) — 1,581 citations
• Deep Learners Benefit More from Out-of-Distribution Examples (2011) — 169 citations
• Unsupervised and Transfer Learning Challenge: a Deep Learning Approach (2011) — 241 citations
• The Manifold Tangent Classifier (2011) — 285 citations