Feb 10, 2020 · We introduce a semidefinite programming hierarchy to estimate the global and local Lipschitz constant of a multiple layer deep neural network.
We introduce a semidefinite programming hierarchy to estimate the global and local Lipschitz constant of a multiple layer deep neural network. The novelty is to ...
*** The papers presents a hierarchical optimization approach, based on polynomial optimization, to compute an upper bound on the Lipschtiz constant of ReLU ...
Dec 6, 2020 · We introduce a semidefinite programming hierarchy to estimate the global and local Lipschitz constant of a multiple layer deep neural network.
We introduce a semidefinite programming hierarchy to estimate the global and local Lipschitz constant of a multiple layer deep neural network. The novelty is to ...
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks. Tong Chen, Jean Lasserre, Victor Magron, Edouard Pauwels. Keywords: Abstract Paper ...
Jun 9, 2020 · The Lipschitz constant of a network plays an important role in many applications of deep learning, such as robustness certification and ...
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What is the Lipschitz constant for ReLU?
Semialgebraic optimization for lipschitz constants of relu networks. T Chen ... Robustness verification of neural networks using polynomial optimization.
This paper explores methods for verifying the properties of Binary Neural Networks (BNNs), focusing on robustness against adversarial attacks. Paper · Add Code ...
This work introduces LiPopt, a polynomial optimization framework for computing increasingly tighter upper bounds on the Lipschitz constant of neural ...