kzhai / PyNPB
Non-parametric Bayesian in Python, including Indian buffet process (IBP), hierarchical Dirichlet process (HDP).
☆73Updated 9 years ago
Alternatives and similar repositories for PyNPB:
Users that are interested in PyNPB are comparing it to the libraries listed below
- Deep exponential families (DEFs)☆55Updated 7 years ago
- Implementation of stochastic variational inference for Bayesian hidden Markov models.☆65Updated 7 years ago
- Bernoulli Embeddings for Text☆83Updated 7 years ago
- Stochastic gradient routines for Theano☆102Updated 6 years ago
- The information sieve for discrete variables.☆36Updated 8 years ago
- Deep generative models for semi-supervised learning.☆108Updated 8 years ago
- ☆88Updated 8 years ago
- Implementation of the VRAE☆58Updated 3 years ago
- Collaborative filtering with the GP-LVM☆25Updated 9 years ago
- ☆46Updated 11 years ago
- An implementation of Mikolov's word2vec in Python using Theano and Lasagne.☆37Updated 7 years ago
- Variational and semi-supervised neural network toppings for Lasagne☆208Updated 8 years ago
- code for stochastic expectation propagation☆16Updated 9 years ago
- Code to train Importance Weighted Autoencoders on MNIST and OMNIGLOT☆206Updated 9 years ago
- Randomized embeddings for extreme learning☆24Updated 9 years ago
- Train an Infinite Restricted Boltzmann Machine☆28Updated 6 years ago
- ☆45Updated 7 years ago
- Torch implementation of the Deep Network for Global Optimization (DNGO)☆51Updated 8 years ago
- Poisson-Gamma dynamical systems☆16Updated 8 years ago
- Collection of useful, re-used routines.☆45Updated 7 years ago
- A variational recurrent neural network implementation in tensorflow☆103Updated 7 years ago
- Talk for SciPy2015 "Deep Learning: Tips From The Road"☆44Updated 9 years ago
- This is code associated with the paper: Broderick, T, Boyd, N, Wibisono, A, Wilson, AC, and Jordan, MI. Streaming variational Bayes. Neur…☆41Updated 10 years ago
- Repo for a paper about constructing priors on very deep models.☆72Updated 8 years ago
- Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.☆150Updated 8 years ago
- Optimizers for machine learning☆182Updated last year
- Bidirectional Helmholtz Machines☆41Updated 9 years ago
- ☆64Updated 7 years ago
- Reasonably-okay-performing implementation of a GAN and an adversarial autoencoder on MNIST.☆29Updated 9 years ago
- Social Poisson Factorization☆26Updated 5 years ago