7 citations · 12 across the 8 of their papers we have counts for
9 papers
Large Vision Models Can Solve Mental Rotation Problems
Sebastian Ray Mason, Anders Gjølbye, Phillip Chavarria Højbjerg +2
Mental rotation is a key test of spatial reasoning in humans and has been central to understanding how perception supports cognition. Despite the success of modern vision transform…
Cat, Rat, Meow: On the Alignment of Language Model and Human Term-Similarity Judgments
Lorenz Linhardt, Tom Neuhäuser, Lenka Tětková +1
Small and mid-sized generative language models have gained increasing attention. Their size and availability make them amenable to being analyzed at a behavioral as well as a repre…
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.…
On convex decision regions in deep network representations
Lenka Tětková, Thea Brüsch, Teresa Karen Scheidt +5
Current work on human-machine alignment aims at understanding machine-learned latent spaces and their correspondence to human representations. G{ä}rdenfors' conceptual spaces is a…
Robustness of Visual Explanations to Common Data Augmentation
Lenka Tětková, Lars Kai Hansen
As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but…