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A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Implements supervised topic models with a categorical response.
Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
Deep exponential families (DEFs)
This implements topics that change over time (Dynamic Topic Models) and a model of how individual documents predict that change.
Context Selection for Embedding Models
Code for the icml paper "zero inflated exponential family embedding"
Discussion of Durante et al for JSM 2017. Includes factorial network model generalization.
The pdf and LaTeX for each paper (and sometimes the code and data used to generate the figures).
Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
topic model visualization
Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.
Online variational Bayes for latent Dirichlet allocation (LDA)
This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data.
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.
Exposure Matrix Factorization: modeling user exposure in recommendation
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.
Turbo topics find significant multiword phrases in topics.
Latent Dirichlet allocation (LDA) with bumping variational inference.
create a browser of a corpus using a topic model; original TMVE implementation (static pages)
Online inference for the Hierarchical Dirichlet Process. Fits hierarchical Dirichlet process topic models to massive data. The algorithm determines the number of topics.
collaborative topic modeling
This implements the discrete infinite logistic normal, a Bayesian nonparametric topic model that finds correlated topics.
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.
This implements variational inference for the correlated topic model.
The old version of the latent Dirichlet allocation package for R