7 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 TÄtková +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…
How Redundant Is the Transformer Stack in Speech Representation Models?
Teresa Dorszewski, Albert Kjøller Jacobsen, Lenka TÄtková +1
Self-supervised speech representation models, particularly those leveraging transformer architectures, have demonstrated remarkable performance across various tasks such as speech…
Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks
Teresa Dorszewski, Lenka TÄtková, 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…
Convexity-based Pruning of Speech Representation Models
Teresa Dorszewski, Lenka TÄtková, 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 TÄtková, 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…