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 …

Inference capabilities of machine learning (ML) systems skyrocketed in recent years, now
playing a pivotal role in various aspect of society. The goal in statistical learning is to use …

This paper proposes an Information Bottleneck theory based filter pruning method that uses
a statistical measure called Mutual Information (MI). The MI between filters and class labels …

Federated learning is a decentralized topology of deep learning, that trains a shared model
through data distributed among each client (like mobile phones, wearable devices), in order …

Although deep neural networks have been immensely successful, there is no
comprehensive theoretical understanding of how they work or are structured. As a result …

Data scarcity is a common and challenging issue when working with Artificial Intelligence
solutions, especially those including Deep Learning (DL) models for tasks such as image …

The training dynamics of hidden layers in deep learning are poorly understood in theory.
Recently, the Information Plane (IP) was proposed to analyze them, which is based on the …

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

Action recognition is a key algorithmic part of emerging on-the-edge smart video
surveillance and security systems. Skeleton-based action recognition is an attractive …

Deep learning methods have had outstanding performances in various fields. A fundamental
query is why they are so effective. Information theory provides a potential answer by …