Search Results for author: Jane X. Wang

Found 9 papers, 5 papers with code

Meta-Learned Models of Cognition

1 code implementation12 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.

Bayesian Inference Meta-Learning

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

2 code implementations22 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.

Few-Shot Learning In-Context Learning

Meta-learning in natural and artificial intelligence

no code implementations26 Nov 2020 Jane X. Wang

Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community.

Meta-Learning

Temporal Difference Uncertainties as a Signal for Exploration

no code implementations5 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.

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