380 citations · 3.3k across the 50 of their papers we have counts for
53 papers
Meta-in-context learning in large language models
Julian Coda-Forno, Marcel Binz, Zeynep Akata +3
Large language models have shown tremendous performance in a variety of tasks. In-context learning -- the ability to improve at a task after being provided with a number of demonst…
DiscoGen: Learning to Discover Gene Regulatory Networks
Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein +11
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inhe…
Fine-tuning language models to find agreement among humans with diverse preferences
Michiel A. Bakker, Martin J. Chadwick, Hannah R. Sheahan +8
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static…
A Unified Theory of Dual-Process Control
Ted Moskovitz, Kevin Miller, Maneesh Sahani +1
Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment an…
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Anthony Zador, Sean Escola, Blake Richards +24
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in…
Adaptive patch foraging in deep reinforcement learning agents
Nathan J. Wispinski, Andrew Butcher, Kory W. Mathewson +3
Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimiz…