TeaPearce / Expressive_Priors_in_BNNsLinks
UAI paper 'Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions'
☆11Updated 5 years ago
Alternatives and similar repositories for Expressive_Priors_in_BNNs
Users that are interested in Expressive_Priors_in_BNNs are comparing it to the libraries listed below
Sorting:
- Official code for the ICLR 2021 paper Neural ODE Processes☆72Updated 3 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆42Updated 10 months ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 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
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 3 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆26Updated last year
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆31Updated 3 years ago
- Bayesian active learning with EPIG data acquisition☆32Updated last month
- Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks☆10Updated 2 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago
- ☆15Updated 2 years ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆21Updated 3 years ago
- ☆14Updated last year
- Kernel Identification Through Transformers☆12Updated 2 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆84Updated 4 years ago
- Normalizing Flows with a resampled base distribution☆47Updated 2 years ago
- Variational Implicit Processes☆9Updated 6 years ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆35Updated 2 years ago
- PyTorch implementation of Stein Variational Gradient Descent☆45Updated last year
- Implementation of the Convolutional Conditional Neural Process☆123Updated 4 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 6 years ago
- Library for Deep Gaussian Processes based on GPflow☆19Updated 5 years ago