Search Results for author:
Found 5 papers, 3 papers with code
Spectral Norm of Convolutional Layers with Circular and Zero Paddings
This paper leverages the use of \emph{Gram iteration} an efficient, deterministic, and differentiable method for computing spectral norm with an upper bound guarantee.
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
The certified radius in this context is a crucial indicator of the robustness of models.
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
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 nowadays a real scientific challenge.
A Unified Algebraic Perspective on Lipschitz Neural Networks
Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant.
Ranked #1 on
Provable Adversarial Defense
on CIFAR-100
A Dynamical System Perspective for Lipschitz Neural Networks
The Lipschitz constant of neural networks has been established as a key quantity to enforce the robustness to adversarial examples.
