Second Workshop on Representational Alignment (Re$^2$-Align)

Brian Cheung, Dota Tianai Dong, Erin Grant, Ilia Sucholutsky, Lukas Muttenthaler, Siddharth Suresh

Keywords: cognitive science, neuroscience, alignment, representation learning

Abstract: Both natural and artificial intelligences form representations of the world that they use to reason, make decisions, and communicate. Despite extensive research across machine learning, neuroscience, and cognitive science, it remains unclear what the most appropriate ways are to compare and align the representations of intelligence systems. In the second edition of the Workshop on Representational Alignment (Re$^2$-Align), we bring together researchers from diverse fields who study representational alignment to make concrete progress on this set of open interdisciplinary problems. We invite researchers across the machine learning, neuroscience, and cognitive science communities to participate and contribute to the workshop in two main ways: (1) via contributed papers and participation in structured discussions during the workshop; and (2) by participating in the workshop hackathon.

Submission Number: 66