Getting aligned on representational alignment

no code implementations • 18 Oct 2023 • Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Iris Groen, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths

Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields.

Navigate Transfer Learning

Brain-inspired learning in artificial neural networks: a review

no code implementations • 18 May 2023 • Samuel Schmidgall, Jascha Achterberg, Thomas Miconi, Louis Kirsch, Rojin Ziaei, S. Pardis Hajiseyedrazi, Jason Eshraghian

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics.

Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture

no code implementations • 21 Mar 2023 • Jascha Achterberg, Danyal Akarca, Moataz Assem, Moritz Heimbach, Duncan E. Astle, John Duncan

There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide variety of cognitive tasks but without requiring fine-tuning on individual problem spaces and domains.