blei-lab/factorial-network-models blei-lab/factorial-network-models

  • Stars
    star
    9
  • Rank 1,939,727 (Top 39 %)
  • Language
    Jupyter Notebook
  • Created over 7 years ago
  • Updated about 7 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
blei-lab/factorial-network-models
github

blei-lab

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

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

More Repositories

1

edward

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Jupyter Notebook
4,834
star
2

onlineldavb

Online variational Bayes for latent Dirichlet allocation (LDA)
Python
300
star
3

dtm

This implements topics that change over time (Dynamic Topic Models) and a model of how individual documents predict that change.
Shell
196
star
4

lda-c

This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data.
C
166
star
5

hdp

Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.
C++
150
star
6

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.
C++
147
star
7

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.
Python
144
star
8

causal-text-embeddings

Software and data for "Using Text Embeddings for Causal Inference"
Python
122
star
9

deconfounder_tutorial

Jupyter Notebook
87
star
10

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.
JavaScript
77
star
11

publications

The pdf and LaTeX for each paper (and sometimes the code and data used to generate the figures).
TeX
73
star
12

class-slda

Implements supervised topic models with a categorical response.
C++
64
star
13

variational-smc

Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
Python
63
star
14

deep-exponential-families

Deep exponential families (DEFs)
C++
56
star
15

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.
C++
49
star
16

turbotopics

Turbo topics find significant multiword phrases in topics.
Python
46
star
17

ars-reparameterization

Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
Jupyter Notebook
38
star
18

zero-inflated-embedding

Code for the icml paper "zero inflated exponential family embedding"
Python
28
star
19

context-selection-embedding

Context Selection for Embedding Models
Python
27
star
20

ctm-c

This implements variational inference for the correlated topic model.
C
21
star
21

deconfounder_public

Jupyter Notebook
18
star
22

treeffuser

Treeffuser is an easy-to-use package for probabilistic prediction on tabular data with tree-based diffusion models.
Jupyter Notebook
13
star
23

markovian-score-climbing

Python
8
star
24

diln

This implements the discrete infinite logistic normal, a Bayesian nonparametric topic model that finds correlated topics.
C
6
star
25

poisson-influence-factorization

Jupyter Notebook
4
star
26

Riken_tutorial

Jupyter Notebook
4
star
27

circuitry

Python
3
star