Publications associées à Coordinate descent

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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Coordinate descent

Graph Chatbot

Chattez avec Graph Search

Data Downloaded via Parachute from a NASA Super-Pressure Balloon

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

Regularization Techniques for Low-Resource Machine Translation

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Polynomial-time universality and limitations of deep learning

Stable Nonconvex-Nonconcave Training via Linear Interpolation

Practical Byzantine-resilient Stochastic Gradient Descent

Proximal Point Imitation Learning

Data Downloaded via Parachute from a NASA Super-Pressure Balloon

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Practical Byzantine-resilient Stochastic Gradient Descent

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Stable Nonconvex-Nonconcave Training via Linear Interpolation

Regularization Techniques for Low-Resource Machine Translation

Polynomial-time universality and limitations of deep learning

Proximal Point Imitation Learning