2013
NIPS
NeurIPS 2013
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
Abstract
In this paper we investigate the use of Langevin Monte Carlo methods on the probability simplex and propose a new method, Stochastic gradient Riemannian Langevin dynamics, which is simple to implement and can be applied online. We apply this method to latent Dirichlet allocation in an online setting, and demonstrate that it achieves substantial performance improvements to the state of the art online variational Bayesian methods.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Mathematics & Optimization
🧭
Keyword Pioneer
— stochastic gradient riemannian langevin dynamics
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
🐣
Hot Topic Early Bird
— stochastic gradient
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Deep Learning > Models > Generative Models
Deep Learning > Models > Variational Inference
Mathematics & Optimization > Optimization > Stochastic Methods
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Variational Inference
Machine Learning > Learning Types > Topic Modeling