team-approx-bayes / dnn2gp
Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
☆48Updated 5 years ago
Alternatives and similar repositories for dnn2gp:
Users that are interested in dnn2gp are comparing it to the libraries listed below
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆82Updated 4 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)☆33Updated last year
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated 9 months ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆38Updated 5 years ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 4 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆48Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- PyTorch implementation of Stein Variational Gradient Descent☆43Updated last year
- Laplace Redux -- Effortless Bayesian Deep Learning☆42Updated last year
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 3 years ago
- Limitations of the Empirical Fisher Approximation☆47Updated 2 weeks ago
- Natural Gradient, Variational Inference☆29Updated 5 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Codes for "Understanding and Accelerating Particle-Based Variational Inference" (ICML-19)☆22Updated 5 years ago
- Implementation of the Functional Neural Process models☆43Updated 4 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 3 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 5 years ago
- ☆23Updated 3 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 4 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆75Updated last year
- Code to minimize the Variational Contrastive Divergence (VCD)☆29Updated 5 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago