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cs.LG2025

Concept-Guided Interpretability via Neural Chunking

Shuchen Wu, Stephan Alaniz, Shyamgopal Karthik +3

Neural networks are often described as black boxes, reflecting the significant challenge of understanding their internal workings and interactions. We propose a different perspecti…

cs.LG2025

Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences

Shuchen Wu, Mirko Thalmann, Peter Dayan +2

Humans excel at learning abstract patterns across different sequences, filtering out irrelevant details, and transferring these generalized concepts to new sequences. In contrast,…

cs.LG2025

Centaur: a foundation model of human cognition

Marcel Binz, Elif Akata, Matthias Bethge +37

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational model…

cs.LG2024

Simplifying Latent Dynamics with Softly State-Invariant World Models

Tankred Saanum, Peter Dayan, Eric Schulz

To solve control problems via model-based reasoning or planning, an agent needs to know how its actions affect the state of the world. The actions an agent has at its disposal ofte…

cs.LG2024

Next state prediction gives rise to entangled, yet compositional representations of objects

Tankred Saanum, Luca M. Schulze Buschoff, Peter Dayan +1

Compositional representations are thought to enable humans to generalize across combinatorially vast state spaces. Models with learnable object slots, which encode information abou…