36 citations · 199 across the 37 of their papers we have counts for
4 papers · 1 filter
Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning
Niklas Babendererde, Moritz Fuchs, Camila Gonzalez +2
Federated and Continual Learning have emerged as potential paradigms for the robust and privacy-aware use of Deep Learning in dynamic environments. However, Client Drift and Catast…
Improving robustness and calibration in ensembles with diversity regularization
Hendrik Alexander Mehrtens, Camila González, Anirban Mukhopadhyay
Calibration and uncertainty estimation are crucial topics in high-risk environments. We introduce a new diversity regularizer for classification tasks that uses out-of-distribution…
Understanding Interpretability by generalized distillation in Supervised Classification
Adit Agarwal, K. K. Shukla, Arjan Kuijper +1
The ability to interpret decisions taken by Machine Learning (ML) models is fundamental to encourage trust and reliability in different practical applications. Recent interpretatio…
A Theory of Diagnostic Interpretation in Supervised Classification
Anirban Mukhopadhyay
Interpretable deep learning is a fundamental building block towards safer AI, especially when the deployment possibilities of deep learning-based computer-aided medical diagnostic…