Julien Rabin

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 5 papers; trusts 239 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it; #4,352 of 21,575 by trust here, #5,941 by citations
• Generative Models — 50 citations into it; #1,428 of 1,956 by trust here, #1,681 by citations
• Artificial Intelligence — 23 citations into it; #24,357 of 35,345 by trust here, #25,809 by citations
• Computer Graphics and Computer-Aided Design — 11 citations into it
• Applied Mathematics — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it; #2,181 of 3,147 by trust here, #2,743 by citations
• Computational Mechanics — 7 citations into it; #1,086 of 10,001 by trust here, #1,409 by citations
• Media Technology — 7 citations into it

Whom Julien Rabin trusts overall

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

1. Cédric Villani
2. Antonin Chambolle
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Yoshua Bengio
7. Kaiming He
8. Yann LeCun
9. Christian Szegedy
10. Karen Simonyan

Who trusts Julien Rabin overall

1. Mohammad Rostami
2. Soheil Kolouri
3. Marco Cuturi
4. G. Peyr'e
5. Brendan Pass
6. Filippo Santambrogio
7. Nicolas Bonnotte
8. François-Pierre Paty
9. Gustavo Kunde Rohde
10. Serban Stan

Papers from Julien Rabin's perspective

Ordered by how strongly the researchers Julien Rabin 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. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Wasserstein Barycenter and Its Application to Texture Mixing (2011) — 767 citations
• Detecting Overfitting of Deep Generative Networks via Latent Recovery (2019) — 107 citations
• Sliced and Radon Wasserstein Barycenters of Measures (2014) — 726 citations
• Revisiting Precision and Recall Definition for Generative Model Evaluation (2019) — 20 citations
• Wasserstein regularization of imaging problem (2011) — 52 citations