1 code implementation • 28 Feb 2024 • Julian Coda-Forno, Marcel Binz, Jane X. Wang, Eric Schulz
Large language models (LLMs) have significantly advanced the field of artificial intelligence.
1 code implementation • 12 Apr 2023 • Marcel Binz, Ishita Dasgupta, Akshay Jagadish, Matthew Botvinick, Jane X. Wang, Eric Schulz
Meta-learning is a framework for learning learning algorithms through repeated interactions with an environment as opposed to designing them by hand.
2 code implementations • 22 Apr 2022 • Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya Singh, Pierre H. Richemond, Jay McClelland, Felix Hill
In further experiments, we found that naturalistic data distributions were only able to elicit in-context learning in transformers, and not in recurrent models.
no code implementations • 8 Apr 2022 • Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen, Nicholas A. Roy, Stephanie C. Y. Chan, DJ Strouse, Jane X. Wang, Andrea Banino, Felix Hill
We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments.
no code implementations • 5 Apr 2022 • Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, Felix Hill
In summary, explanations can support the in-context learning of large LMs on challenging tasks.
1 code implementation • 7 Dec 2021 • Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, Felix Hill
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents.
1 code implementation • 4 Feb 2021 • Jane X. Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, Francis Song, Gavin Buttimore, David P. Reichert, Neil Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matthew Botvinick
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning.
no code implementations • 26 Nov 2020 • Jane X. Wang
Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community.
no code implementations • 5 Oct 2020 • Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, Andre Barreto, Razvan Pascanu
Instead, we incorporate it as an intrinsic reward and treat exploration as a separate learning problem, induced by the agent's temporal difference uncertainties.