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Found 3 papers, 1 papers with code
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
We consider a deep matrix factorization model of covariance matrices trained with the Bures-Wasserstein distance.
High dimensional change-point detection: a complete graph approach
We propose a complete graph-based, change-point detection algorithm to detect change of mean and variance from low to high-dimensional online data with a variable scanning window.
Online Graph-Based Change-Point Detection for High Dimensional Data
Online change-point detection (OCPD) is important for application in various areas such as finance, biology, and the Internet of Things (IoT).
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