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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

Mémoire à tores magnétiques

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Magnon-Assisted Magnetization Reversal of Ni81Fe19 Nanostripes on Y3Fe5O12 with Different Interfaces

A 128-kbit GC-eDRAM With Negative Boosted Bootstrap Driver for 11.3x Lower-Refresh Frequency at a 2.5% Area Overhead in 28-nm FD-SOI

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

Chaosity: Understanding Contemporary NUMA-architectures

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

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

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

AstriFlash: A Flash-Based System for Online Services

Design for Test With Unreliable Memories by Restoring the Beauty of Randomness

Magnon-Assisted Magnetization Reversal of Ni81Fe19 Nanostripes on Y3Fe5O12 with Different Interfaces

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

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

A 128-kbit GC-eDRAM With Negative Boosted Bootstrap Driver for 11.3x Lower-Refresh Frequency at a 2.5% Area Overhead in 28-nm FD-SOI

AstriFlash: A Flash-Based System for Online Services

Design for Test With Unreliable Memories by Restoring the Beauty of Randomness

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

Chaosity: Understanding Contemporary NUMA-architectures

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale