22 citations · 36 across the 11 of their papers we have counts for
4 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…
Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG
Manshan Guo, Bhavin Choksi, Sari Sadiya +4
In contrast to human vision, artificial neural networks (ANNs) remain relatively susceptible to adversarial attacks. To address this vulnerability, efforts have been made to transf…
FovEx: Human-Inspired Explanations for Vision Transformers and Convolutional Neural Networks
Mahadev Prasad Panda, Matteo Tiezzi, Martina Vilas +3
Explainability in artificial intelligence (XAI) remains a crucial aspect for fostering trust and understanding in machine learning models. Current visual explanation techniques, su…
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…