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Machine learning in cybersecurity: a comprehensive survey
Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …
Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Bayesian neural networks (BNN) and deep ensembles are principled approaches to
estimate the predictive uncertainty of a deep learning model. However their practicality in …
estimate the predictive uncertainty of a deep learning model. However their practicality in …
Warm: On the benefits of weight averaged reward models
Aligning large language models (LLMs) with human preferences through reinforcement
learning (RLHF) can lead to reward hacking, where LLMs exploit failures in the reward …
learning (RLHF) can lead to reward hacking, where LLMs exploit failures in the reward …
Wild patterns: Ten years after the rise of adversarial machine learning
Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …
applications, ranging from computer vision to computer security. In most of these …
Towards deep learning models resistant to adversarial attacks
Recent work has demonstrated that deep neural networks are vulnerable to adversarial
examples---inputs that are almost indistinguishable from natural data and yet classified …
examples---inputs that are almost indistinguishable from natural data and yet classified …
A closer look at memorization in deep networks
We examine the role of memorization in deep learning, drawing connections to capacity,
generalization, and adversarial robustness. While deep networks are capable of memorizing …
generalization, and adversarial robustness. While deep networks are capable of memorizing …
Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks
Transferability captures the ability of an attack against a machine-learning model to be
effective against a different, potentially unknown, model. Empirical evidence for …
effective against a different, potentially unknown, model. Empirical evidence for …
Gradient descent maximizes the margin of homogeneous neural networks
In this paper, we study the implicit regularization of the gradient descent algorithm in
homogeneous neural networks, including fully-connected and convolutional neural …
homogeneous neural networks, including fully-connected and convolutional neural …
Generalization in deep learning
This chapter provides theoretical insights into why and how deep learning can generalize
well, despite its large capacity, complexity, possible algorithmic instability, non-robustness …
well, despite its large capacity, complexity, possible algorithmic instability, non-robustness …