MFreidank / pysgmcmcLinks
Bayesian Deep Learning with Stochastic Gradient MCMC Methods
☆38Updated 4 years ago
Alternatives and similar repositories for pysgmcmc
Users that are interested in pysgmcmc are comparing it to the libraries listed below
Sorting:
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated last year
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 4 years ago
- A community repository for benchmarking Bayesian methods☆111Updated 3 years ago
- ☆40Updated 6 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 7 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 4 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 7 years ago
- Code required to reproduce the experiments in Auxiliary Variational MCMC☆17Updated 7 years ago
- Automated Scalable Bayesian Inference☆131Updated 3 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 4 years ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 7 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 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
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- Tensorflow implementation of Stein Variational Gradient Descent (SVGD)☆26Updated 7 years ago
- Deep neural network kernel for Gaussian process☆212Updated 5 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 3 years ago
- Package implementing various parametric and nonparametric methods for conditional density estimation☆197Updated 2 years ago
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design☆37Updated 4 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- The code for Meta Learning for SGMCMC☆25Updated 6 years ago
- Neural Processes implementation for 1D regression☆64Updated 6 years ago
- CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.☆78Updated last year
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 5 years ago