Marc Sebban

Artificial Intelligence · 2012–2016

A researcher in the Science corpus: 5 papers; trusts 276 researchers and is trusted by 213 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #3,711 of 35,345 by trust here, #4,445 by citations
• Computer Vision and Pattern Recognition — 44 citations into it; #2,570 of 21,575 by trust here, #3,378 by citations
• Representation & Self-Supervised Learning — 39 citations into it; #523 of 3,145 by trust here, #622 by citations
• Statistical Learning Theory — 17 citations into it
• Optimization & Training — 14 citations into it; #2,074 of 2,601 by trust here, #2,378 by citations
• Signal Processing — 13 citations into it
• Computational Mechanics — 11 citations into it
• Statistics and Probability — 8 citations into it

Whom Marc Sebban trusts overall

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

1. Geoffrey E. Hinton
2. Brian Kulis
3. Yoshua Bengio
4. Bernhard Schölkopf
5. Alex Krizhevsky
6. Yann LeCun
7. Thorsten Joachims
8. Diederik P. Kingma
9. Kaiming He
10. Ruslan Salakhutdinov

Who trusts Marc Sebban overall

1. Mohammad Rostami
2. Wen Li
3. Wouter M. Kouw
4. Baochen Sun
5. Soheil Kolouri
6. Li Niu
7. Aurélien Bellet
8. Yuhua Chen
9. Gabriella Csurka
10. Shiv Ram Dubey

Papers from Marc Sebban's perspective

Ordered by how strongly the researchers Marc Sebban 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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Unsupervised Visual Domain Adaptation Using Subspace Alignment (2013) — 1,373 citations
• A Survey on Metric Learning for Feature Vectors and Structured Data (2013) — 714 citations
• Similarity Learning for Provably Accurate Sparse Linear Classification (2012) — 57 citations
• Theoretical Analysis of Domain Adaptation with Optimal Transport (2016) — 222 citations
• Metric Learning (2015) — 196 citations