clinicalml / structuredinferenceLinks
Structured Inference Networks for Nonlinear State Space Models
☆273Updated 8 years ago
Alternatives and similar repositories for structuredinference
Users that are interested in structuredinference are comparing it to the libraries listed below
Sorting:
- Deep Markov Models☆133Updated 6 years ago
- code for Structured Variational Autoencoders☆350Updated 7 years ago
- Kalman Variational Auto-Encoder☆136Updated 6 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆249Updated last year
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago
- Code for the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"☆379Updated 8 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆151Updated 6 years ago
- Bayesian nonparametric machine learning for Python☆228Updated 2 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 8 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago
- Tools for loading standard data sets in machine learning☆204Updated 2 years ago
- Code for "Sequential Neural Models with Stochastic Layers"☆117Updated 8 years ago
- Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al☆219Updated 6 years ago
- What My Deep Model Doesn't Know...☆116Updated 9 years ago
- A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)☆353Updated 8 years ago
- Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.☆83Updated 5 years ago
- pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.☆216Updated 6 years ago
- Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch☆361Updated 5 years ago
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆408Updated last year
- Neural Processes implementation for 1D regression☆64Updated 6 years ago
- I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth☆185Updated 11 years ago
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.☆43Updated 11 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆106Updated 7 years ago
- ☆96Updated 7 years ago
- ☆240Updated 8 years ago
- Deep Gaussian Processes in Python☆235Updated 4 years ago
- Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.☆191Updated 6 years ago
- Deep Gaussian Processes in matlab☆93Updated 4 years ago
- Edward content including papers, posters, and talks☆92Updated 4 years ago
- Variational Fourier Features☆85Updated 4 years ago