7 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks
Teresa Dorszewski, Lenka Tětková, Lorenz Linhardt +1
Understanding how neural networks align with human cognitive processes is a crucial step toward developing more interpretable and reliable AI systems. Motivated by theories of huma…
How Redundant Is the Transformer Stack in Speech Representation Models?
Teresa Dorszewski, Albert Kjøller Jacobsen, Lenka Tětková +1
Self-supervised speech representation models, particularly those leveraging transformer architectures, have demonstrated remarkable performance across various tasks such as speech…
Convexity-based Pruning of Speech Representation Models
Teresa Dorszewski, Lenka Tětková, Lars Kai Hansen
Speech representation models based on the transformer architecture and trained by self-supervised learning have shown great promise for solving tasks such as speech and speaker rec…
Challenges in explaining deep learning models for data with biological variation
Lenka Tětková, Erik Schou Dreier, Robin Malm +1
Much machine learning research progress is based on developing models and evaluating them on a benchmark dataset (e.g., ImageNet for images). However, applying such benchmark-succe…
Knowledge graphs for empirical concept retrieval
Lenka Tětková, Teresa Karen Scheidt, Maria Mandrup Fogh +3
Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz.\ as a tool for personalized explainability.…