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Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Probabilistic matrix factorization (PMF) is a powerful method for modeli...
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High-Dimensional Probability Estimation with Deep Density Models
One of the fundamental problems in machine learning is the estimation of...
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Gaussian Process Kernels for Pattern Discovery and Extrapolation
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Fast Exact Inference for Recursive Cardinality Models
Cardinality potentials are a generally useful class of high order potent...
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Ranking via Sinkhorn Propagation
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On Nonparametric Guidance for Learning Autoencoder Representations
Unsupervised discovery of latent representations, in addition to being u...
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Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability dis...
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Elliptical slice sampling
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