3 citations · 5 across the 7 of their papers we have counts for
7 papers
Keypoint Counting Classifiers: Turning Vision Transformers into Self-Explainable Models Without Training
Kristoffer Wickstrøm, Teresa Dorszewski, Siyan Chen +3
Current approaches for designing self-explainable models (SEMs) require complicated training procedures and specific architectures which makes them impractical. With the advance of…
Mammo-CLIP Dissect: A Framework for Analysing Mammography Concepts in Vision-Language Models
Suaiba Amina Salahuddin, Teresa Dorszewski, Marit Almenning Martiniussen +7
Understanding what deep learning (DL) models learn is essential for the safe deployment of artificial intelligence (AI) in clinical settings. While previous work has focused on pix…
From Colors to Classes: Emergence of Concepts in Vision Transformers
Teresa Dorszewski, Lenka Tětková, Robert Jenssen +2
Vision Transformers (ViTs) are increasingly utilized in various computer vision tasks due to their powerful representation capabilities. However, it remains understudied how ViTs p…
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…
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…