all AI news
Score Matching for Truncated Density Estimation on a Manifold
April 15, 2024, 4:44 a.m. | Daniel J. Williams, Song Liu
stat.ML updates on arXiv.org arxiv.org
Abstract: When observations are truncated, we are limited to an incomplete picture of our dataset. Recent methods propose to use score matching for truncated density estimation, where the access to the intractable normalising constant is not required. We present a novel extension of truncated score matching to a Riemannian manifold with boundary. Applications are presented for the von Mises-Fisher and Kent distributions on a two dimensional sphere in $\mathbb{R}^3$, as well as a real-world application of …
abstract access arxiv dataset extension manifold novel stat.me stat.ml type
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Associate Data Engineer
@ Nominet | Oxford/ Hybrid, GB
Data Science Senior Associate
@ JPMorgan Chase & Co. | Bengaluru, Karnataka, India