3 papers
cs.LG2026
Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI
Sophia N. Wilson, Andrew Millard, Guðrún Fjóla Guðmundsdóttir +2
This position paper argues that the machine learning community must move from preaching to practising data frugality for responsible artificial intelligence (AI) development. For t…
cs.LG2026
An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks
Hallgrimur Thorsteinsson, Valdemar J Henriksen, Daniel I R Cruz +2
As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasin…
cs.LG2026
Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis
Pedram Bakhtiarifard, Sophia N. Wilson, Mahmoud Afifi +2
Training large-scale deep neural networks (DNNs) is resource-intensive, making model compression a practical necessity. The widely accepted ''learning as compression'' hypothesis p…