1 citations · 2 across the 7 of their papers we have counts for
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A Generalized Information Bottleneck Theory of Deep Learning
Charles Westphal, Stephen Hailes, Mirco Musolesi
The Information Bottleneck (IB) principle offers a compelling theoretical framework to understand how neural networks (NNs) learn. However, its practical utility has been constrain…
Feature Selection for Network Intrusion Detection
Charles Westphal, Stephen Hailes, Mirco Musolesi
Network Intrusion Detection (NID) remains a key area of research within the information security community, while also being relevant to Machine Learning (ML) practitioners. The la…
Mutual Information Preserving Neural Network Pruning
Charles Westphal, Stephen Hailes, Mirco Musolesi
Pruning has emerged as the primary approach used to limit the resource requirements of large neural networks (NNs). Since the proposal of the lottery ticket hypothesis, researchers…
Partial Information Decomposition for Data Interpretability and Feature Selection
Charles Westphal, Stephen Hailes, Mirco Musolesi
In this paper, we introduce Partial Information Decomposition of Features (PIDF), a new paradigm for simultaneous data interpretability and feature selection. Contrary to tradition…
TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning
Charles Westphal, Stephen Hailes, Mirco Musolesi
Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL). These variables must efficiently capture the information n…