Laurent Valentin Jospin

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 479 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 104 citations into it
• Artificial Intelligence — 65 citations into it
• Probabilistic & Bayesian Methods — 34 citations into it
• Aerospace Engineering — 18 citations into it
• Media Technology — 17 citations into it
• Optimization & Training — 17 citations into it
• 3D Vision & Pose — 14 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Laurent Valentin Jospin trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Radford M. Neal
4. Yann LeCun
5. Alex Krizhevsky
6. Max Welling
7. Yoshua Bengio
8. Kaiming He
9. Ilya Sutskever
10. Alex Graves

Who trusts Laurent Valentin Jospin overall

1. Moloud Abdar

Papers from Laurent Valentin Jospin's perspective

Ordered by how strongly the researchers Laurent Valentin Jospin 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. 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

• A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation (2020) — 348 citations
• Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users (2020) — 908 citations