Mathias Lécuyer

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 48 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #34,742 of 35,345 by trust here, #11,624 by citations
• Safety, Robustness & Interpretability — 91 citations into it; #2,185 of 2,251 by trust here, #1,023 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Signal Processing — 16 citations into it
• Privacy & Security — 12 citations into it; #698 of 709 by trust here, #292 by citations
• Generative Models — 9 citations into it
• Hardware and Architecture — 9 citations into it
• Model Architectures — 6 citations into it

Whom Mathias Lécuyer trusts overall

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

1. Ian J. Goodfellow
2. Christian Szegedy
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Nicolas Papernot
7. Yoshua Bengio
8. Yann LeCun
9. Ilya Sutskever
10. Kaiming He

Who trusts Mathias Lécuyer overall

1. Bai Li
2. Huan Zhang
3. Changyou Chen
4. Adnan Siraj Rakin
5. Jeremy M. Cohen
6. Sungyoon Lee
7. Elahe Arani
8. Alexander Levine
9. Chandra Thapa
10. Hadi Salman

Papers from Mathias Lécuyer's perspective

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

• Certified Robustness to Adversarial Examples with Differential Privacy (2018) — 1,063 citations
• On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning (2018) — 20 citations