microsoft / deterministic-variational-inferenceLinks
Sample code for running deterministic variational inference to train Bayesian neural networks
☆101Updated 7 years ago
Alternatives and similar repositories for deterministic-variational-inference
Users that are interested in deterministic-variational-inference are comparing it to the libraries listed below
Sorting:
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆86Updated 5 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆42Updated 3 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated last year
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 7 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 6 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 7 years ago
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆54Updated last year
- Understanding normalizing flows☆132Updated 6 years ago
- PyTorch implementation of Neural Processes☆88Updated 6 years ago
- The collection of recent papers about variational inference☆84Updated 6 years ago
- Hypergradient descent☆147Updated last year
- Implementation of the Functional Neural Process models☆42Updated 5 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆122Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆112Updated 4 years ago
- Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)☆34Updated 2 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Pytorch implementation of Block Neural Autoregressive Flow☆181Updated 4 years ago
- Deep convolutional gaussian processes.☆82Updated 6 years ago
- PyTorch Implementation of Neural Statistician☆61Updated 3 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 6 years ago
- Experiments for the Neural Autoregressive Flows paper☆125Updated 4 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning☆93Updated 5 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 5 years ago
- Limitations of the Empirical Fisher Approximation☆49Updated 9 months ago
- Pytorch implementation of Neural Processes for functions and images☆234Updated 3 years ago
- a python implementation of various versions of the information bottleneck, including automated parameter searching☆130Updated 5 years ago
- TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".☆60Updated 6 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 5 years ago