teodor-moldovan / dpclusterLinks
Efficient Dirichlet process clustering
☆26Updated 12 years ago
Alternatives and similar repositories for dpcluster
Users that are interested in dpcluster are comparing it to the libraries listed below
Sorting:
- Implementation of stochastic variational inference for Bayesian hidden Markov models.☆68Updated 8 years ago
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.☆43Updated 11 years ago
- Hyperparameter optimization with approximate gradient☆66Updated 4 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 8 years ago
- Sticky hierarchical Dirichlet process hidden Markov model for time series denoising☆50Updated 9 years ago
- A suite of boosting algorithms for the online learning setting.☆66Updated 8 years ago
- Hierarchical Mixture of Experts,Mixture Density Neural Network☆45Updated 8 years ago
- Bayesian Weight Uncertainty Dense Layer for Keras☆47Updated 8 years ago
- MADE: Masked Autoencoder for Distribution Estimation☆103Updated 5 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago
- Additive Gaussian Process Bandits - version 1.0☆27Updated 8 years ago
- pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.☆216Updated 6 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 11 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆251Updated last year
- AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)☆35Updated 7 years ago
- Deep Gaussian Processes in matlab☆92Updated 4 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆110Updated 7 years ago
- Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood☆95Updated 8 years ago
- ☆74Updated 6 years ago
- Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.☆191Updated 6 years ago
- TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper☆51Updated 6 years ago
- ☆68Updated 3 weeks ago
- Code for "Sequential Neural Models with Stochastic Layers"☆117Updated 9 years ago
- Non-parametric Bayesian in Python, including Indian buffet process (IBP), hierarchical Dirichlet process (HDP).☆73Updated 10 years ago
- Gaussian Process optimization algorithm for Hyperopt☆24Updated 11 years ago
- ☆40Updated 6 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 8 years ago
- Variational inference for Gaussian mixture models☆35Updated 12 years ago
- modular implementation of new algorithm☆13Updated 11 years ago
- Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…☆30Updated 5 years ago