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edward
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.onlineldavb
Online variational Bayes for latent Dirichlet allocation (LDA)dtm
This implements topics that change over time (Dynamic Topic Models) and a model of how individual documents predict that change.lda-c
This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data.hdp
Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.ctr
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.online-hdp
Online inference for the Hierarchical Dirichlet Process. Fits hierarchical Dirichlet process topic models to massive data. The algorithm determines the number of topics.causal-text-embeddings
Software and data for "Using Text Embeddings for Causal Inference"deconfounder_tutorial
hlda
This implements hierarchical latent Dirichlet allocation, a topic model that finds a hierarchy of topics. The structure of the hierarchy is determined by the data.publications
The pdf and LaTeX for each paper (and sometimes the code and data used to generate the figures).class-slda
Implements supervised topic models with a categorical response.variational-smc
Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)deep-exponential-families
Deep exponential families (DEFs)DynamicPoissonFactorization
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.turbotopics
Turbo topics find significant multiword phrases in topics.ars-reparameterization
Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)zero-inflated-embedding
Code for the icml paper "zero inflated exponential family embedding"context-selection-embedding
Context Selection for Embedding Modelsctm-c
This implements variational inference for the correlated topic model.tmv
topic model visualizationdeconfounder_public
factorial-network-models
Discussion of Durante et al for JSM 2017. Includes factorial network model generalization.markovian-score-climbing
diln
This implements the discrete infinite logistic normal, a Bayesian nonparametric topic model that finds correlated topics.poisson-influence-factorization
Riken_tutorial
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