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4 papers · 2 filters
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
Ye Wang, David B. Dunson
Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning. One common approach is to suppose that the observed data are close to a l…
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
Xin Yuan, Ricardo Henao, Ephraim L. Tsalik +2
We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce…
A Generative Model for Deep Convolutional Learning
Yunchen Pu, Xin Yuan, Lawrence Carin
A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding effi…
Adaptive Randomized Dimension Reduction on Massive Data
Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee +1
The scalability of statistical estimators is of increasing importance in modern applications. One approach to implementing scalable algorithms is to compress data into a low dimens…