3 citations · 5 across the 28 of their papers we have counts for
14 papers · 1 filter
Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive
Brian B. Moser, Ahmed Anwar, Tobias Christian Nauen +5
Continual learning regularizers like EWC fight forgetting by penalizing changes from previous-task parameters with per-parameter importance, typically diagonal Fisher values. Per-p…
The Gentle Collapse: Distributional Metrics for Continual Learning
Ahmed Anwar, Andreas Wagner, Federico Raue +2
Accuracy degradation is the standard metric for Catastrophic Forgetting (CF), however, it records only whether forgetting occurred or not. It saturates at the extremes and collapse…
TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
Dayananda Herurkar, Federico Raue, Joachim Folz +2
Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and…
Hyperspherical Forward-Forward with Prototypical Representations
Shalini Sarode, Brian Moser, Joachim Folz +4
The Forward-Forward (FF) algorithm presents a compelling, bio-inspired alternative to backpropagation. However, while efficient in training, it has a computationally prohibitive in…
PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors
Brian B. Moser, Shalini Sarode, Federico Raue +6
Dataset distillation (DD) promises compact yet faithful synthetic data, but existing approaches often inherit the inductive bias of a single teacher model. As dataset size increase…
SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection
Brian B. Moser, Tobias C. Nauen, Arundhati S. Shanbhag +4
The goal of coreset selection is to identify representative subsets of datasets for efficient model training. Yet, existing approaches paradoxically require expensive training-base…