37 citations · 39 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…