Search Results for author:
Found 8 papers, 1 papers with code
Global Decision-Making via Local Economic Transactions
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.
Probing BERT’s priors with serial reproduction chains
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.
Learning a face space for experiments on human identity
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.
Connecting Context-specific Adaptation in Humans to Meta-learning
This work connects the context-sensitive nature of cognitive control to a method for meta-learning with context-conditioned adaptation.
Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes
Many approaches to hierarchical reinforcement learning aim to identify sub-goal structure in tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task.
Evaluating computational models of explanation using human judgments
We evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments.
The Author-Topic Model for Authors and Documents
A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors.
