Most data is automatically collected and only ever" seen" by algorithms. Yet, data
compressors preserve perceptual fidelity rather than just the information needed by …

Much of the field of Machine Learning exhibits a prominent set of failure modes, including
vulnerability to adversarial examples, poor out-of-distribution (OoD) detection …

Abstract The Information Bottleneck (IB) objective uses information theory to formulate a task-
performance versus robustness trade-off. It has been successfully applied in the standard …

In this paper, we propose a binarized neural network learning method (BiDet) for efficient
object detection. Conventional network binarization methods directly quantize the weights …

Joint source and channel coding (JSCC) for image transmission has attracted increasing
attention due to its robustness and high efficiency. However, the existing deep JSCC …

This survey is for the remembrance of one of the creators of the information bottleneck
theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information …

In the Information Bottleneck (IB), when tuning the relative strength between compression
and prediction terms, how do the two terms behave, and what's their relationship with the …

This paper introduces an innovative approach to analyzing and improving multi-hop
reasoning in AI systems by drawing inspiration from Hamiltonian mechanics. We propose a …

Intuitively, one way to make classifiers more robust to their input is to have them depend less
sensitively on their input. The Information Bottleneck (IB) tries to learn compressed …

Information bottleneck (IB) and privacy funnel (PF) are two closely related optimization
problems which have found applications in machine learning, design of privacy algorithms …