4 citations · 8 across the 20 of their papers we have counts for
6 papers · 1 filter
Multi-agent cooperation through learning-aware policy gradients
Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald +6
Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation among self-interested, independent lear…
A Complexity-Based Theory of Compositionality
Eric Elmoznino, Thomas Jiralerspong, Yoshua Bengio +1
Compositionality is believed to be fundamental to intelligence. In humans, it underlies the structure of thought, language, and higher-level reasoning. In AI, compositional represe…
In-context learning and Occam's razor
Eric Elmoznino, Tom Marty, Tejas Kasetty +5
A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees for generalization without further assumpt…
Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch +5
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high tempora…
When can transformers compositionally generalize in-context?
Seijin Kobayashi, Simon Schug, Yassir Akram +5
Many tasks can be composed from a few independent components. This gives rise to a combinatorial explosion of possible tasks, only some of which might be encountered during trainin…
Does learning the right latent variables necessarily improve in-context learning?
Sarthak Mittal, Eric Elmoznino, Leo Gagnon +4
Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights, suggesting avenues for efficiently solving new tasks.…