Stochastic EM for generic topic modeling using probabilistic programming
Probabilistic topic models are a versatile class of models for discovering latent themes in document collections through unsupervised learning. Conventional inferential methods lack the scaling capabilities necessary for extensions to large-scale applications. In recent years Stochastic Expectation...
Main Author: | Saberi Nasseri, Robin |
---|---|
Format: | Others |
Language: | English |
Published: |
Uppsala universitet, Statistiska institutionen
2021
|
Subjects: | |
Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447568 |
Similar Items
-
Calibration of Probabilistic Predictive Models
by: Widmann, David
Published: (2020) -
Stochastic Gradient Descent for Efficient Logistic Regression
by: Thorleifsson, Alexander
Published: (2016) -
Evaluation of the Robustness of Different Classifiers under Low- and High-Dimensional Settings
by: Lantz, Linnea
Published: (2019) -
A study on the application of machine learning algorithms in stochastic optimal control
by: Huang, Xin
Published: (2019) -
A stochastic differential equation derived from evolutionary game theory
by: Treacy, Brian
Published: (2019)