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This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...
The way our brain learns to disentangle complex signals into unambiguous concepts is fascinating but remains largely unknown. There is evidence, however, that hierarchical neural representations play a key role in the cortex. This thesis investigates biolo ...
Meta-learning is the core capability that enables intelligent systems to rapidly generalize their prior ex-perience to learn new tasks. In general, the optimization-based methods formalize the meta-learning as a bi-level optimization problem, that is a nes ...
Side-channel CPU disassembly is a side-channel attack that allows an adversary to recover instructions executed by a processor. Not only does such an attack compromise code confidentiality, it can also reveal critical information on the system’s internals. ...
Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...
This paper investigates the theory of robustness against adversarial attacks. We focus on randomized classifiers (i.e. classifiers that output random variables) and provide a thorough analysis of their behavior through the lens of statistical learning theo ...
Unmanned aerial vehicles (UAVs) are widely deployed in air navigation, where numerous applications use them for safety-of-life and positioning, navigation, and timing tasks. Consequently, GPS spoofing attacks are more and more frequent. The aim of this wor ...
Introduction: The unprecedented speed and scale of the COVID-19 pandemic necessitated the rapid implementation of untested public health measures to mitigate the consequences of viral spread. In the 8 months that have passed since the first recognized case ...
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...
Transformer oil-based nanofluids are known to have higher thermal conductivity and heat transfer performance compared to conventional transformer oils. In this study, four different types of transformer oil-based nanofluids are synthesized using the well-k ...
