Abstract. Most classification methods assume that the samples are drawn independently and identically from an unknown data generating distribution, yet this assumption is violated ...
We propose a probabilistic, generative account of configural learning phenomena in classical conditioning. Configural learning experiments probe how animals discriminate and gener...
Aaron C. Courville, Nathaniel D. Daw, David S. Tou...
An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn...
We present a new domain for unsupervised learning: automatically customizing the computer to a specific melodic performer by merely listening to them improvise. We also describe B...
Adaptor grammars (Johnson et al., 2007b) are a non-parametric Bayesian extension of Probabilistic Context-Free Grammars (PCFGs) which in effect learn the probabilities of entire s...