We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to loca...
Recent research in machine learning has focused on breaking audio spectrograms into separate sources of sound using latent variable decompositions. These methods require that the ...
We introduce the Spherical Admixture Model (SAM), a Bayesian topic model for arbitrary 2 normalized data. SAM maintains the same hierarchical structure as Latent Dirichlet Allocat...
Joseph Reisinger, Austin Waters, Bryan Silverthorn...
We investigate the sparse eigenvalue problem which arises in various fields such as machine learning and statistics. Unlike standard approaches relying on approximation of the l0n...
Deep learning has been successfully applied to perform non-linear embedding. In this paper, we present supervised embedding techniques that use a deep network to collapse classes....
Martin Renqiang Min, Laurens van der Maaten, Zinen...