all AI news
Variational Approach for Efficient KL Divergence Estimation in Dirichlet Mixture Models
March 20, 2024, 4:42 a.m. | Samyajoy Pal, Christian Heumann
cs.LG updates on arXiv.org arxiv.org
Abstract: This study tackles the efficient estimation of Kullback-Leibler (KL) Divergence in Dirichlet Mixture Models (DMM), crucial for clustering compositional data. Despite the significance of DMMs, obtaining an analytically tractable solution for KL Divergence has proven elusive. Past approaches relied on computationally demanding Monte Carlo methods, motivating our introduction of a novel variational approach. Our method offers a closed-form solution, significantly enhancing computational efficiency for swift model comparisons and robust estimation evaluations. Validation using real and …
abstract arxiv clustering cs.lg data divergence math.st significance solution stat.ml stat.th study tractable type
More from arxiv.org / cs.LG updates on arXiv.org
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
2 days, 13 hours ago |
arxiv.org
Bagging Provides Assumption-free Stability
2 days, 13 hours ago |
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
RL Analytics - Content, Data Science Manager
@ Meta | Burlingame, CA
Research Engineer
@ BASF | Houston, TX, US, 77079