This paper presents a novel theory for learning generic prior models from a set of observed natural images based on a minimax entropy theory that the authors studied in modeling t...
Abstract. This paper argues that accrual should be modelled in terms of reasoning about the application of preferences to sets of arguments, and shows how such reasoning can be for...
We develop a framework based on Bayesian model averaging to explain how animals cope with uncertainty about contingencies in classical conditioning experiments. Traditional accoun...
Aaron C. Courville, Nathaniel D. Daw, Geoffrey J. ...
Creating ensembles of random but "realistic" topologies for complex systems is crucial for many tasks such as benchmark generation and algorithm analysis. In general, exp...
Current approaches to explicit user modelling are generally time consuming and tedious for the user. Oftentimes poor usability and overly long questionnaires deter the end user fro...