Publications associées à Algorithme du gradient stochastique

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Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

We propose the Square Attack, a new score-based black-box

l_2

and

l_\infty

adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where ...

2020

Algorithme du gradient stochastique

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Lipschitz constant estimation for Neural Networks via sparse polynomial optimization

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

Square Attack: a query-efficient black-box adversarial attack via random search

Robust Distributed Learning

FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces

MATHICSE Technical Report : Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines

Sparse Polynomial Chaos expansions using variational relevance vector machines

Principal Component Analysis By Optimization Of Symmetric Functions Has No Spurious Local Optima

Accurate deep neural network inference using computational phase-change memory

MATHICSE Technical Report : Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

Square Attack: a query-efficient black-box adversarial attack via random search

Lipschitz constant estimation for Neural Networks via sparse polynomial optimization

FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

Robust Distributed Learning

Sparse Polynomial Chaos expansions using variational relevance vector machines

Principal Component Analysis By Optimization Of Symmetric Functions Has No Spurious Local Optima

Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces

Accurate deep neural network inference using computational phase-change memory