[图书][B] Accelerator Architecture for Secure and Energy Efficient Machine learning
MH Samavatian - 2022 - search.proquest.com
ML applications are driving the next computing revolution. In this context both performance
and security are crucial. We propose hardware/software co-design solutions for addressing …
and security are crucial. We propose hardware/software co-design solutions for addressing …
[图书][B] Structural Defense Techniques in Adversarial Machine Learning
C Bakiskan - 2022 - search.proquest.com
Over the last decade, deep neural networks (DNNs) have become an increasingly popular
choice for researchers looking to take on previously unsolved problems. With the popularity …
choice for researchers looking to take on previously unsolved problems. With the popularity …
Can Collaborative Learning Be Private, Robust and Scalable?
D Rueckert, G Kaissis - … Collaborative, and Federated Learning, and Affordable … - Springer
In federated learning for medical image analysis, the safety of the learning protocol is
paramount. Such settings can often be compromised by adversaries that target either the …
paramount. Such settings can often be compromised by adversaries that target either the …
[PDF][PDF] Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead
M SHAFIQUE - arxiv.org
ABSTRACT Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep
Learning (DL) is already present in many applications ranging from computer vision for …
Learning (DL) is already present in many applications ranging from computer vision for …
Adversarial Attacks and Defenses against Deep Learning in Cybersecurity
Adversarial attacks and defenses on cyber-physical systems is basically an AI (artifi cial
intelligence) technique that mimics the human mind, ie, the process of human thinking …
intelligence) technique that mimics the human mind, ie, the process of human thinking …