kekeblom / DeepCGPLinks
Deep convolutional gaussian processes.
☆78Updated 5 years ago
Alternatives and similar repositories for DeepCGP
Users that are interested in DeepCGP are comparing it to the libraries listed below
Sorting:
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago
- Implementations of the ICML 2017 paper (with Yarin Gal)☆38Updated 7 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- see https://github.com/thangbui/geepee for a faster implementation☆37Updated 8 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 6 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 4 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 6 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 4 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated last year
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆105Updated 7 years ago
- Code to minimize the Variational Contrastive Divergence (VCD)☆29Updated 6 years ago
- ☆40Updated 6 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 4 years ago
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆54Updated 8 months ago
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆67Updated 5 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆150Updated 6 years ago
- Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)☆33Updated 2 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 5 years ago
- Variational Message Passing for Structured VAE (Code for ICLR 2018 paper)☆44Updated 7 years ago
- Neural Processes implementation for 1D regression☆65Updated 6 years ago
- Natural Gradient, Variational Inference☆29Updated 5 years ago
- Code for "A-NICE-MC: Adversarial Training for MCMC"☆125Updated 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