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  • License
    MIT License
  • Created about 6 years ago
  • Updated over 4 years ago

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Repository Details

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dtm

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lda-c

This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data.
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Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.
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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.
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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.
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8

causal-text-embeddings

Software and data for "Using Text Embeddings for Causal Inference"
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9

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.
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10

publications

The pdf and LaTeX for each paper (and sometimes the code and data used to generate the figures).
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class-slda

Implements supervised topic models with a categorical response.
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12

variational-smc

Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
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13

deep-exponential-families

Deep exponential families (DEFs)
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14

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.
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15

turbotopics

Turbo topics find significant multiword phrases in topics.
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16

ars-reparameterization

Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
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17

zero-inflated-embedding

Code for the icml paper "zero inflated exponential family embedding"
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18

context-selection-embedding

Context Selection for Embedding Models
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19

ctm-c

This implements variational inference for the correlated topic model.
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20

deconfounder_public

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21

treeffuser

Treeffuser is an easy-to-use package for probabilistic prediction on tabular data with tree-based diffusion models.
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22

factorial-network-models

Discussion of Durante et al for JSM 2017. Includes factorial network model generalization.
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23

markovian-score-climbing

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24

diln

This implements the discrete infinite logistic normal, a Bayesian nonparametric topic model that finds correlated topics.
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25

poisson-influence-factorization

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26

Riken_tutorial

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circuitry

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