Inductive learning is impossible without overhypothe-ses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. The hierarchi-cal approach also addresses a common question about Bayesian models of cognition: where do the priors come from? To illustrate our claims, we consider two specific kinds of overhypotheses — overhypotheses about fea-ture variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into on-tological kinds like objects and substances.Charles Kemp, Amy Perfors and Joshua B. Tenenbau
. A Machine can only learn if it is biased in some way. Typically the bias is supplied by hand, for ...
We demonstrate the potential of using hierarchical Bayesian methods to relate models and data in the...
Categorization, or classification, is a fundamental problem in both cognitive psychology and machine...
Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered...
Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008....
The human ability to learn quickly about causal relationships requires abstract knowledge that provi...
We present a hierarchical Bayesian framework for modeling the acqui-sition of verb argument construc...
We present a hierarchical Bayesian framework for modeling the acquisition of verb argument construct...
International audienceHierarchical processing is pervasive in the brain, but its computational signi...
Introduction: The need for hierarchical models Those of us who study human cognition have no easy ta...
In previous work [3] we have proposed Hierarchical Bayesian Networks (HBNs) as an extension of Bay...
The use of abstract higher-level knowledge (overhypotheses) allows humans to learn quickly from spar...
Hierarchical processing is pervasive in the brain, but its computational significance for learning u...
This article demonstrates the potential of using hierarchical Bayesian methods to relate models and ...
. A Machine can only learn if it is biased in some way. Typically the bias is supplied by hand, for ...
We demonstrate the potential of using hierarchical Bayesian methods to relate models and data in the...
Categorization, or classification, is a fundamental problem in both cognitive psychology and machine...
Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered...
Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008....
The human ability to learn quickly about causal relationships requires abstract knowledge that provi...
We present a hierarchical Bayesian framework for modeling the acqui-sition of verb argument construc...
We present a hierarchical Bayesian framework for modeling the acquisition of verb argument construct...
International audienceHierarchical processing is pervasive in the brain, but its computational signi...
Introduction: The need for hierarchical models Those of us who study human cognition have no easy ta...
In previous work [3] we have proposed Hierarchical Bayesian Networks (HBNs) as an extension of Bay...
The use of abstract higher-level knowledge (overhypotheses) allows humans to learn quickly from spar...
Hierarchical processing is pervasive in the brain, but its computational significance for learning u...
This article demonstrates the potential of using hierarchical Bayesian methods to relate models and ...
. A Machine can only learn if it is biased in some way. Typically the bias is supplied by hand, for ...
We demonstrate the potential of using hierarchical Bayesian methods to relate models and data in the...
Categorization, or classification, is a fundamental problem in both cognitive psychology and machine...