Alexandre Araujo
Alexandre Araujo
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Theoretical evidence for adversarial robustness through randomization
R Pinot, L Meunier, A Araujo, H Kashima, F Yger, C Gouy-Pailler, J Atif
Advances in Neural Information Processing Systems 32, 2019
Robust neural networks using randomized adversarial training
A Araujo, L Meunier, R Pinot, B Negrevergne
arXiv preprint arXiv:1903.10219, 2019
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
A Araujo, B Negrevergne, Y Chevaleyre, J Atif
Proceedings of the AAAI Conference on Artificial Intelligence 35, 2020
Training compact deep learning models for video classification using circulant matrices
A Araujo, B Negrevergne, Y Chevaleyre, J Atif
The European Conference on Computer Vision (ECCV) Workshops, 2018
A Dynamical System Perspective for Lipschitz Neural Networks
L Meunier, B Delattre, A Araujo, A Allauzen
International Conference on Machine Learning (ICML), 2021
Advocating for multiple defense strategies against adversarial examples
A Araujo, L Meunier, R Pinot, B Negrevergne
ECML PKDD 2020 Workshops, 165-177, 2020
Understanding and Training Deep Diagonal Circulant Neural Networks
A Araujo, B Negrevergne, Y Chevaleyre, J Atif
24th European Conference on Artificial Intelligence - ECAI 2020, 2019
A Unified Algebraic Perspective on Lipschitz Neural Networks
A Araujo, AJ Havens, B Delattre, A Allauzen, B Hu
International Conference on Learning Representations, 2023
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
H Xue, A Araujo, B Hu, Y Chen
arXiv preprint arXiv:2305.16494, 2023
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
B Delattre, Q Barthélemy, A Araujo, A Allauzen
arXiv preprint arXiv:2305.16173, 2023
Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
R Ettedgui, A Araujo, R Pinot, Y Chevaleyre, J Atif
arXiv preprint arXiv:2206.01715, 2022
Building Compact and Robust Deep Neural Networks with Toeplitz Matrices
A Araujo
PhD Thesis, 2021
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