We develop hierarchical, probabilistic models for objects, the parts composing them, and the visual scenes surrounding them. Our approach couples topic models originally developed...
Erik B. Sudderth, Antonio Torralba, William T. Fre...
We develop a new component analysis framework, the Noisy-Or Component Analyzer (NOCA), that targets high-dimensional binary data. NOCA is a probabilistic latent variable model tha...
Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. B...
We present some greedy learning algorithms for building sparse nonlinear regression and classification models from observational data using Mercer kernels. Our objective is to dev...
Prasanth B. Nair, Arindam Choudhury 0002, Andy J. ...
Behavioural models offer the ability to simulate autonomous agents like organisms and living beings. Psychological studies have showed that the human behaviour can be described by...