Thibault Laugel

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 25 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #20,656 of 35,345 by trust here, #14,181 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #2,009 of 2,251 by trust here, #1,819 by citations
• Computer Vision and Pattern Recognition — 7 citations into it

Whom Thibault Laugel trusts overall

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

1. Cynthia Rudin
2. Yann LeCun
3. David Martens
4. Ryan D. Turner
5. Riccardo Guidotti
6. Yoshua Bengio
7. L. Eon Bottou
8. Geoffrey E. Hinton
9. Satoshi Hara
10. Lisa Anne Hendricks

Who trusts Thibault Laugel overall

1. Ilia Stepin
2. Nadia Burkart
3. Kacper Sokol
4. Amir-Hossein Karimi
5. Sahil Verma
6. Giulia Vilone
7. Arnaud Van Looveren
8. Christoph Molnar
9. Q. Liao
10. Ana Lucic

Papers from Thibault Laugel's perspective

Ordered by how strongly the researchers Thibault Laugel 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations (2019) — 231 citations
• Comparison-Based Inverse Classification for Interpretability in Machine Learning (2018) — 133 citations
• Defining Locality for Surrogates in Post-hoc Interpretablity (2018) — 101 citations
• Inverse Classification for Comparison-based Interpretability in Machine Learning (2017) — 121 citations