In this paper we present a theoretical analysis to understand sparse filtering, a recent and
effective algorithm for unsupervised learning. The aim of this research is not to show …

We explore whether useful temporal neural generative models can be learned from
sequential data without back-propagation through time. We investigate the viability of a more …

This thesis studies a family of unsupervised learning algorithms called feature distribution
learning and their extension to perform covariate shift adaptation. Unsupervised learning is …

It is common statistical learning practice to build models, nowadays largely connectionist
models, on very large, static, and fully annotated datasets of identically and independently …