Statistical inference with deep latent variable models
Finding a suitable way to represent information in a dataset is one of the fundamental problems in Artificial Inelegance. With limited labeled information, unsupervised learning algorithms help to discover useful representations. One of the applications of such models is imputation, where missing values are estimated by learning the underlying correlations in a dataset. This thesis explores two ofFinding a suitable way to represent information in a dataset is one of the fundamental problems in Artificial Intelligence. With limited labeled information, unsupervised learning algorithms help to discover useful representations. One of the applications of such models is imputation, where missing values are estimated by learning the underlying correlations in a dataset. This thesis explores two