markvdw / convgpLinks
Convolutional Gaussian processes based on GPflow.
☆95Updated 7 years ago
Alternatives and similar repositories for convgp
Users that are interested in convgp are comparing it to the libraries listed below
Sorting:
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Deep convolutional gaussian processes.☆78Updated 5 years ago
- Implementations of the ICML 2017 paper (with Yarin Gal)☆38Updated 7 years ago
- Neural Processes implementation for 1D regression☆65Updated 6 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 8 years ago
- ☆40Updated 6 years ago
- Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…☆30Updated 4 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- see https://github.com/thangbui/geepee for a faster implementation☆37Updated 8 years ago
- Code for "A-NICE-MC: Adversarial Training for MCMC"☆125Updated 6 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆105Updated 7 years ago
- Implementation of Stochastic Gradient MCMC algorithms☆41Updated 8 years ago
- Variational Message Passing for Structured VAE (Code for ICLR 2018 paper)☆44Updated 7 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆150Updated 6 years ago
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆67Updated 5 years ago
- Variational Fourier Features☆86Updated 4 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 4 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Updated 9 years ago
- ☆37Updated 5 years ago
- TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network☆182Updated 6 years ago
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.☆43Updated 11 years ago
- Repo for a paper about constructing priors on very deep models.☆73Updated 8 years ago
- Deep Generative Models with Stick-Breaking Priors☆95Updated 9 years ago
- Code for "Sequential Neural Models with Stochastic Layers"☆117Updated 8 years ago
- Code for "How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks"☆101Updated 9 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 4 years ago
- Annealed Importance Sampling (AIS) for generative models.☆16Updated 6 years ago