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...
We address the problem of understanding an indoor scene from a single image in terms of recovering the layouts of the faces (floor, ceiling, walls) and furniture. A major challeng...
Maximum a posteriori (MAP) inference in graphical models requires that we maximize the sum of two terms: a data-dependent term, encoding the conditional likelihood of a certain la...
The presence of autocorrelation provides a strong motivation for using relational learning and inference techniques. Autocorrelation is a statistical dependence between the values...
Networks are becoming a unifying framework for modeling complex systems and network inference problems are frequently encountered in many fields. Here, I develop and apply a gener...