Numerous deep learning algorithms have been inspired by and understood via the notion of
information bottleneck, where unnecessary information is (often implicitly) minimized while …

We review the current literature concerned with information plane (IP) analyses of neural
network (NN) classifiers. While the underlying information bottleneck theory and the claim …

Adversarial data augmentation has shown promise for training robust deep neural networks
against unforeseen data shifts or corruptions. However, it is difficult to define heuristics to …

In this theory paper, we investigate training deep neural networks (DNNs) for classification
via minimizing the information bottleneck (IB) functional. We show that the resulting …

Information bottleneck (IB) is a technique for extracting information in one random variable X
that is relevant for predicting another random variable Y. IB works by encoding X in a …

Information bottleneck (IB) is a method for extracting information from one random variable $
X $ that is relevant for predicting another random variable $ Y $. To do so, IB identifies an …

It is known that deep neural networks, trained for the classification of non-sensitive target
attributes, can reveal sensitive attributes of their input data through internal representations …

The information bottleneck (IB) method is a technique for extracting information that is
relevant for predicting the target random variable from the source random variable, which is …

Biomedical signals, encapsulating vital physiological information, are pivotal in elucidating
human traits and conditions, serving as a cornerstone for advancing human–machine …

Bottleneck problems are an important class of optimization problems that have recently
gained increasing attention in the domain of machine learning and information theory. They …