Clément Bénard
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 2 papers; trusts 31 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 31 citations into it
- Statistical Learning Theory — 15 citations into it
- Safety, Robustness & Interpretability — 10 citations into it
- Statistics and Probability — 10 citations into it
- Algorithms & Complexity — 7 citations into it
- Computational Theory and Mathematics — 5 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it
- Molecular Biology — 5 citations into it
Whom Clément Bénard trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Leo Breiman
- Bin Yu
- J. Ross Quinlan
- Jerome H. Friedman
- R. Rivest
- Marjolein Fokkema
- Robert Tibshirani
- Trevor J. Hastie
- Niko Speybroeck
- Peter Clark
Who trusts Clément Bénard overall
Papers from Clément Bénard's perspective
Ordered by how strongly the researchers Clément Bénard trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Regression Shrinkage and Selection via the Lasso
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Interpretable Random Forests via Rule Extraction (2020) — 77 citations
- SIRUS: making random forests interpretable (2019) — 10 citations