7 papers
Atlas 2 -- Foundation models for clinical deployment
Maximilian Alber, Timo Milbich, Alexandra Carpen-Amarie +24
Pathology foundation models substantially advanced the possibilities in computational pathology --- yet tradeoffs in terms of performance, robustness, and computational requirement…
Aligning Machine and Human Visual Representations across Abstraction Levels
Lukas Muttenthaler, Klaus Greff, Frieda Born +6
Deep neural networks have achieved success across a wide range of applications, including as models of human behavior and neural representations in vision tasks. However, neural ne…
Objective drives the consistency of representational similarity across datasets
Laure Ciernik, Lorenz Linhardt, Marco Morik +3
The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irre…
Human alignment of neural network representations
Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt +2
Today's computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and learning algorithms diff…
Dimensions underlying the representational alignment of deep neural networks with humans
Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü +1
Determining the similarities and differences between humans and artificial intelligence (AI) is an important goal both in computational cognitive neuroscience and machine learning,…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…