84 citations · 178 across the 15 of their papers we have counts for
7 papers · 1 filter
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…
Experimental study of Neural ODE training with adaptive solver for dynamical systems modeling
Alexandre Allauzen, Thiago Petrilli Maffei Dardis, Hannah Plath
Neural Ordinary Differential Equations (ODEs) was recently introduced as a new family of neural network models, which relies on black-box ODE solvers for inference and training. So…
Curriculum learning for data-driven modeling of dynamical systems
Alessandro Bucci, Onofrio Semeraro, Alexandre Allauzen +2
The reliable prediction of the temporal behavior of complex systems is key in numerous scientific fields. This strong interest is however hindered by modeling issues: often, the go…
A Dynamical System Perspective for Lipschitz Neural Networks
Laurent Meunier, Blaise Delattre, Alexandre Araujo +1
The Lipschitz constant of neural networks has been established as a key quantity to enforce the robustness to adversarial examples. In this paper, we tackle the problem of building…
Document Neural Autoregressive Distribution Estimation
Stanislas Lauly, Yin Zheng, Alexandre Allauzen +1
We present an approach based on feed-forward neural networks for learning the distribution of textual documents. This approach is inspired by the Neural Autoregressive Distribution…