https://github.com/robert-giaquinto/dapper

Fast implementation of DAP topic model using Conjugate Computation Variational Inference
https://github.com/robert-giaquinto/dapper

em-algorithm graphical-models machine-learning natural-language-processing stochastic-gradient-descent stochastic-variational-inference text-analysis text-mining topic-modeling variational-inference

Last synced: 5 months ago
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Fast implementation of DAP topic model using Conjugate Computation Variational Inference

• Host: GitHub
• URL: https://github.com/robert-giaquinto/dapper
• Owner: robert-giaquinto
• License: mit
• Created: 2018-01-04T19:26:22.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-11-19T01:13:25.000Z (over 6 years ago)
• Last Synced: 2024-08-03T18:21:35.899Z (8 months ago)
• Topics: em-algorithm, graphical-models, machine-learning, natural-language-processing, stochastic-gradient-descent, stochastic-variational-inference, text-analysis, text-mining, topic-modeling, variational-inference
• Language: Python
• Homepage:
• Size: 54.1 MB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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• awesome-topic-models - DAPPER - Python implementation of Dynamic Author Persona (DAP) topic model [:page_facing_up:](https://arxiv.org/pdf/1811.01931.pdf) (Models / Miscellaneous topic models)