gpapamak / epsilon_free_inferenceLinks
Code for paper "Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation"
☆32Updated 6 years ago
Alternatives and similar repositories for epsilon_free_inference
Users that are interested in epsilon_free_inference are comparing it to the libraries listed below
Sorting:
- ☆69Updated 7 years ago
- Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…☆30Updated 5 years ago
- Variational Message Passing for Structured VAE (Code for ICLR 2018 paper)☆45Updated 7 years ago
- Repo for a paper about constructing priors on very deep models.☆73Updated 9 years ago
- Deep GPs with GPy☆31Updated 9 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago
- Reducing Reparameterization Gradient Variance code.☆33Updated 8 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- TensorFlow impementation of: Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images☆64Updated 9 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆106Updated 7 years ago
- Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"☆25Updated 7 years ago
- TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network☆183Updated 7 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Implementation of Stochastic Gradient MCMC algorithms☆41Updated 8 years ago
- Torch7 impementation of: Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images☆43Updated 9 years ago
- Code for "A-NICE-MC: Adversarial Training for MCMC"☆125Updated 7 years ago
- Code for "Deep Convolutional Networks as shallow Gaussian Processes"☆39Updated 6 years ago
- Predictive State Recurrent Neural Networks☆18Updated 5 years ago
- Additional kernels that can be used with scikit-learn's Gaussian Process module☆82Updated last year
- RL Experiments from our paper "Backpropagation Through the Void": https://arxiv.org/abs/1711.00123. Lovingly forked from OpenAI's RL Base…☆39Updated 7 years ago
- A collection of Gaussian process models☆30Updated 8 years ago
- MADE: Masked Autoencoder for Distribution Estimation☆102Updated 5 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago
- Variational Fourier Features☆85Updated 4 years ago
- Gaussian Process Random Fields☆21Updated 9 years ago
- Implementations of the ICML 2017 paper (with Yarin Gal)☆38Updated 7 years ago
- Code for Kernel Adaptive Metropolis-Hastings☆33Updated 10 years ago
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.☆43Updated 11 years ago
- Code for "Sequential Neural Models with Stochastic Layers"☆117Updated 8 years ago
- Neural Processes implementation for 1D regression☆64Updated 6 years ago