37 citations · 39 across the 5 of their papers we have counts for
6 papers
The Computational Complexity of Circuit Discovery for Inner Interpretability
Federico Adolfi, Martina G. Vilas, Todd Wareham
Many proposed applications of neural networks in machine learning, cognitive/brain science, and society hinge on the feasibility of inner interpretability via circuit discovery. Th…
Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience
Martina G. Vilas, Federico Adolfi, David Poeppel +1
Inner Interpretability is a promising emerging field tasked with uncovering the inner mechanisms of AI systems, though how to develop these mechanistic theories is still much debat…
MindSet: Vision. A toolbox for testing DNNs on key psychological experiments
Valerio Biscione, Milton L. Montero, Marin Dujmovic +9
Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the…
Successes and critical failures of neural networks in capturing human-like speech recognition
Federico Adolfi, Jeffrey S. Bowers, David Poeppel
Natural and artificial audition can in principle acquire different solutions to a given problem. The constraints of the task, however, can nudge the cognitive science and engineeri…
Computational Complexity of Segmentation
Federico Adolfi, Todd Wareham, Iris van Rooij
Computational feasibility is a widespread concern that guides the framing and modeling of biological and artificial intelligence. The specification of cognitive system capacities i…
Gibbs Sampling with People
Peter M. C. Harrison, Raja Marjieh, Federico Adolfi +5
A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantn…