Yves Grandvalet

Artificial Intelligence · 2004–2018

A researcher in the Science corpus: 6 papers; trusts 272 researchers and is trusted by 222 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #6,064 of 35,345 by trust here, #10,319 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #6,232 of 21,575 by trust here, #8,409 by citations
• Statistics and Probability — 19 citations into it; #10,274 of 12,932 by trust here, #11,487 by citations
• Optimization & Training — 13 citations into it; #1,102 of 2,601 by trust here, #2,052 by citations
• Representation & Self-Supervised Learning — 12 citations into it; #167 of 3,145 by trust here, #572 by citations
• Evaluation & Benchmarks — 10 citations into it
• Statistical Learning Theory — 9 citations into it
• Control and Systems Engineering — 7 citations into it; #1,572 of 8,257 by trust here, #3,865 by citations

Whom Yves Grandvalet trusts overall

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

1. Trevor J. Hastie
2. Robert Tibshirani
3. Alex Krizhevsky
4. Kaiming He
5. Geoffrey E. Hinton
6. Ross B. Girshick
7. Yoshua Bengio
8. Andrew Zisserman
9. Ilya Sutskever
10. Shaoqing Ren

Who trusts Yves Grandvalet overall

1. Mingsheng Long
2. Jian Liang
3. Zhangjie Cao
4. Gideon S. Mann
5. Mohammad Rostami
6. Kihyuk Sohn
7. Percy Liang
8. Jianmin Wang
9. Kuniaki Saito
10. Eran Malach

Papers from Yves Grandvalet's perspective

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

Their papers

• Explicit Inductive Bias for Transfer Learning with Convolutional Networks (2018) — 447 citations
• More efficiency in multiple kernel learning (2007) — 326 citations
• Entropy Regularization (2006) — 108 citations
• Parsimonious additive models (2007) — 38 citations
• Hierarchical Penalization (2007) — 37 citations
• Semi-supervised Learning by Entropy Minimization (2004) — 2,520 citations