no code implementations • ICLR 2018 • Joshua Peterson, Jordan Suchow, Thomas Griffiths
Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on alignment of the latent representation to human psychological representations and the photorealism of the generated images.
no code implementations • ICML 2020 • Michael Chang, Sid Kaushik, S. Matthew Weinberg, Sergey Levine, Thomas Griffiths
This paper seeks to establish a mechanism for directing a collection of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems with a central global objective.
no code implementations • Findings (ACL) 2022 • Takateru Yamakoshi, Thomas Griffiths, Robert Hawkins
Sampling is a promising bottom-up method for exposing what generative models have learned about language, but it remains unclear how to generate representative samples from popular masked language models (MLMs) like BERT.
no code implementations • 27 Nov 2020 • Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas Griffiths
This work connects the context-sensitive nature of cognitive control to a method for meta-learning with context-conditioned adaptation.
no code implementations • 25 Sep 2019 • Sophia Sanborn, Michael Chang, Sergey Levine, Thomas Griffiths
Many approaches to hierarchical reinforcement learning aim to identify sub-goal structure in tasks.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • ICLR 2018 • Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas Griffiths
Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task.
no code implementations • 26 Sep 2013 • Michael Pacer, Joseph Williams, Xi Chen, Tania Lombrozo, Thomas Griffiths
We evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments.
1 code implementation • 11 Jul 2012 • Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth
A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors.