ratschlab / bnn_priorsLinks
Code for the paper "Bayesian Neural Network Priors Revisited"
☆58Updated 4 years ago
Alternatives and similar repositories for bnn_priors
Users that are interested in bnn_priors are comparing it to the libraries listed below
Sorting:
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆44Updated 5 months ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated last year
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 3 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆78Updated 2 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆75Updated 3 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆89Updated last year
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago
- ☆155Updated 3 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 4 years ago
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning☆93Updated 5 years ago
- A community repository for benchmarking Bayesian methods☆111Updated 3 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆38Updated 4 years ago
- ☆15Updated 3 years ago
- ☆250Updated 2 years ago
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- A library for uncertainty quantification based on PyTorch☆122Updated 3 years ago
- Robust initialisation of inducing points in sparse variational GP regression models.☆33Updated 3 years ago
- Bayesian active learning with EPIG data acquisition☆34Updated 2 months ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆21Updated 3 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 7 years ago
- Pytorch implementation of VAEs for heterogeneous likelihoods.☆43Updated 3 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆43Updated last year
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆70Updated last year
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- ☆37Updated 5 years ago