1 citations · 2 across the 15 of their papers we have counts for
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The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
Blaise Delattre, Alexandre Araujo, Quentin Barthélemy +1
Real-life applications of deep neural networks are hindered by their unsteady predictions when faced with noisy inputs and adversarial attacks. The certified radius in this context…
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Blaise Delattre, Quentin Barthélemy, Alexandre Araujo +1
Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowaday…
A Unified Algebraic Perspective on Lipschitz Neural Networks
Alexandre Araujo, Aaron Havens, Blaise Delattre +2
Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant. The goal is to increase and sometimes guarantee the robu…