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Found 9 papers, 2 papers with code
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations
We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states.
Easy Variational Inference for Categorical Models via an Independent Binary Approximation
This approximation makes inference straightforward and fast; using well-known auxiliary variables for probit or logistic regression, the product of binary models admits conjugate closed-form variational inference that is embarrassingly parallel across categories and invariant to category ordering.
Dynamical Wasserstein Barycenters for Time-series Modeling
We propose a dynamical Wasserstein barycentric (DWB) model that estimates the system state over time as well as the data-generating distributions of pure states in an unsupervised manner.
On Matched Filtering for Statistical Change Point Detection
Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms.
Optimal Transport Based Change Point Detection and Time Series Segment Clustering
Two common problems in time series analysis are the decomposition of the data stream into disjoint segments that are each in some sense "homogeneous" - a problem known as Change Point Detection (CPD) - and the grouping of similar nonadjacent segments, a problem that we call Time Series Segment Clustering (TSSC).
Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes
Multi-task/Multi-output learning seeks to exploit correlation among tasks to enhance performance over learning or solving each task independently.
On the Fusion of Compton Scatter and Attenuation Data for Limited-view X-ray Tomographic Applications
To aid in the recovery of the photoelectric information, we draw on our recent method in \cite{r15} and employ a non-local regularization scheme that builds on the fact that mass density is more stably imaged.
Stabilizing dual-energy X-ray computed tomography reconstructions using patch-based regularization
While well motivated physically, the joint recovery of the spatial distribution of photoelectric and Compton properties is severely complicated by the fact that the data are several orders of magnitude more sensitive to Compton scatter coefficients than to photoelectric absorption, so small errors in Compton estimates can create large artifacts in the photoelectric estimate.
Tensor-based formulation and nuclear norm regularization for multi-energy computed tomography
Specifically, we model the multi-spectral unknown as a 3-way tensor where the first two dimensions are space and the third dimension is energy.
