3 papers
cs.LG2026
REDistill: Robust Estimator Distillation for Balancing Robustness and Efficiency
Ondrej Tybl, Lukas Neumann
Knowledge Distillation (KD) transfers knowledge from a large teacher model to a smaller student by aligning their predictive distributions. However, conventional KD formulations -…
cs.SD2026
ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models
Bruno Sienkiewicz, Åukasz Neumann, Mateusz Modrzejewski
Concept-based interpretability methods like TCAV require clean, well-separated positive and negative examples for each concept. Existing music datasets lack this structure: tags ar…
cs.CV2025
Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights
OndÅej Týbl, Lukáš Neumann
Deep learning has revolutionized computer vision, but it achieved its tremendous success using deep network architectures which are mostly hand-crafted and therefore likely subopti…