Benoît Frénay

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 5 papers; trusts 55 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #6,634 of 35,345 by trust here, #8,525 by citations
• Safety, Robustness & Interpretability — 16 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Statistics and Probability — 15 citations into it
• Information Systems — 12 citations into it
• Statistical Learning Theory — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Optimization & Training — 6 citations into it

Whom Benoît Frénay trusts overall

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

1. Carla E. Brodley
2. Jerome H. Friedman
3. Charles Bouveyron
4. P. Lachenbruch
5. Sanford Weisberg
6. Geoffrey I. Webb
7. Mark A. Friedl
8. Lawrence R. Rabiner
9. Ricardo Barandela
10. Robert E. Schapire

Who trusts Benoît Frénay overall

1. Jacob Goldberger
2. Himanshu Kumar
3. Jessa Bekker
4. Omid Kashefi
5. Lucy R. Chai
6. Davood Karimi
7. Kuang-Huei Lee
8. Jiliang Tang
9. Marc-Andre Carbonneau
10. Guibiao Xu

Papers from Benoît Frénay's perspective

Ordered by how strongly the researchers Benoît Frénay 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. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Classification in the Presence of Label Noise: A Survey (2014) — 1,820 citations
• Legal requirements on explainability in machine learning (2020) — 138 citations
• Interpretability of machine learning models and representations: an introduction (2016) — 119 citations
• Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs (2011) — 21 citations
• Clustering Patterns of Urban Built-up Areas with Curves of Fractal Scaling Behaviour (2010) — 63 citations