35 citations · 46 across the 6 of their papers we have counts for
6 papers
Work, Wellbeing, and Choice: Empirical Lessons for AI Futures
Stephanie C. Y. Chan, Adam Bales, Katherine L. Hermann +1
Advances in AI-driven automation have raised questions about how humans might find wellbeing in a world where paid employment is less necessary or less available than before. Paid…
Representation biases: will we achieve complete understanding by analyzing representations?
Andrew Kyle Lampinen, Stephanie C. Y. Chan, Yuxuan Li +1
A common approach in neuroscience is to study neural representations as a means to understand a system -- increasingly, by relating the neural representations to the internal repre…
Understanding Visual Feature Reliance through the Lens of Complexity
Thomas Fel, Louis Bethune, Andrew Kyle Lampinen +2
Recent studies suggest that deep learning models inductive bias towards favoring simpler features may be one of the sources of shortcut learning. Yet, there has been limited focus…
Learned feature representations are biased by complexity, learning order, position, and more
Andrew Kyle Lampinen, Stephanie C. Y. Chan, Katherine Hermann
Representation learning, and interpreting learned representations, are key areas of focus in machine learning and neuroscience. Both fields generally use representations as a means…
On the Foundations of Shortcut Learning
Katherine L. Hermann, Hossein Mobahi, Thomas Fel +1
Deep-learning models can extract a rich assortment of features from data. Which features a model uses depends not only on \emph{predictivity} -- how reliably a feature indicates tr…
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…