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    • Treeffuser is an easy-to-use package for probabilistic prediction on tabular data with tree-based diffusion models.
      Jupyter Notebook
      MIT License
      32512Updated Nov 6, 2024Nov 6, 2024
    • circuitry

      Public
      Python
      MIT License
      0510Updated Oct 30, 2024Oct 30, 2024
    • Development branch of Treeffuser.
      Jupyter Notebook
      MIT License
      3100Updated Jun 16, 2024Jun 16, 2024
    • edward

      Public
      A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
      Jupyter Notebook
      Other
      7594.8k18535Updated Mar 18, 2024Mar 18, 2024
    • Jupyter Notebook
      Apache License 2.0
      1000Updated Mar 10, 2024Mar 10, 2024
    • Python
      MIT License
      0000Updated Feb 28, 2024Feb 28, 2024
    • Jupyter Notebook
      GNU General Public License v3.0
      51911Updated Dec 19, 2022Dec 19, 2022
    • Jupyter Notebook
      MIT License
      1400Updated Jul 28, 2022Jul 28, 2022
    • Implements supervised topic models with a categorical response.
      C++
      GNU General Public License v2.0
      216442Updated Jan 14, 2022Jan 14, 2022
    • tmv

      Public
      topic model visualization
      Python
      GNU General Public License v3.0
      392100Updated Oct 12, 2021Oct 12, 2021
    • Online variational Bayes for latent Dirichlet allocation (LDA)
      Python
      GNU General Public License v3.0
      10230022Updated May 21, 2021May 21, 2021
    • Python
      MIT License
      0800Updated Oct 16, 2020Oct 16, 2020
    • Software and data for "Using Text Embeddings for Causal Inference"
      Python
      MIT License
      1712330Updated Sep 23, 2020Sep 23, 2020
    • Jupyter Notebook
      MIT License
      238720Updated Apr 3, 2020Apr 3, 2020
    • Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
      Python
      MIT License
      136400Updated Apr 29, 2019Apr 29, 2019
    • Deep exponential families (DEFs)
      C++
      105600Updated Feb 8, 2018Feb 8, 2018
    • dtm

      Public
      This implements topics that change over time (Dynamic Topic Models) and a model of how individual documents predict that change.
      Shell
      GNU General Public License v2.0
      7919880Updated Dec 12, 2017Dec 12, 2017
    • Context Selection for Embedding Models
      Python
      112700Updated Nov 2, 2017Nov 2, 2017
    • Code for the icml paper "zero inflated exponential family embedding"
      Python
      Other
      132900Updated Nov 2, 2017Nov 2, 2017
    • Discussion of Durante et al for JSM 2017. Includes factorial network model generalization.
      Jupyter Notebook
      4900Updated Aug 14, 2017Aug 14, 2017
    • The pdf and LaTeX for each paper (and sometimes the code and data used to generate the figures).
      TeX
      117200Updated Apr 25, 2017Apr 25, 2017
    • Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
      Jupyter Notebook
      MIT License
      83910Updated Apr 25, 2017Apr 25, 2017
    • Jupyter Notebook
      1400Updated Mar 8, 2017Mar 8, 2017
    • hdp

      Public
      Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.
      C++
      GNU General Public License v2.0
      4715060Updated Feb 21, 2017Feb 21, 2017
    • embvis

      Public
      Tool for visualizing and browsing over data point embeddings
      JavaScript
      0000Updated Nov 8, 2016Nov 8, 2016
    • lda-c

      Public
      This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data.
      C
      GNU Lesser General Public License v2.1
      9316650Updated Jun 9, 2016Jun 9, 2016
    • Dynamic version of Poisson Factorization (dPF). dPF captures the changing interest of users and the evolution of items over time according to user-item ratings.
      C++
      GNU General Public License v3.0
      304900Updated Feb 22, 2016Feb 22, 2016
    • expo-mf

      Public
      Exposure Matrix Factorization: modeling user exposure in recommendation
      Jupyter Notebook
      40600Updated Feb 15, 2016Feb 15, 2016
    • ctr

      Public
      Collaborative modeling for recommendation. Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings.
      C++
      GNU General Public License v2.0
      4414720Updated Aug 19, 2015Aug 19, 2015
    • Turbo topics find significant multiword phrases in topics.
      Python
      94600Updated Jun 16, 2015Jun 16, 2015