ctallec / pyvarinf
Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
☆359Updated 5 years ago
Alternatives and similar repositories for pyvarinf
Users that are interested in pyvarinf are comparing it to the libraries listed below
Sorting:
- Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832☆251Updated 6 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆140Updated 7 years ago
- Probabilistic Torch is library for deep generative models that extends PyTorch☆887Updated last year
- Implementing Bayes by Backprop☆184Updated 6 years ago
- Papers for Bayesian-NN☆322Updated 5 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆113Updated 6 years ago
- Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"☆245Updated 5 years ago
- PyTorch implementation of "Weight Uncertainty in Neural Networks"☆176Updated 3 years ago
- The collection of recent papers about variational inference☆85Updated 5 years ago
- Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al☆218Updated 6 years ago
- Code for the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"☆378Updated 8 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆120Updated 5 years ago
- This repository contains the code to reproduce the core results from the paper "Adversarial Variational Bayes: Unifying Variational Autoe…☆206Updated 7 years ago
- PyTorch implementations of algorithms for density estimation☆583Updated 4 years ago
- MADE (Masked Autoencoder Density Estimation) implementation in PyTorch☆552Updated 6 years ago
- Understanding normalizing flows☆132Updated 5 years ago
- a repo sharing Bayesian Neural Network recent papers☆216Updated 5 years ago
- A PyTorch library for two-sample tests☆239Updated last year
- Structured Inference Networks for Nonlinear State Space Models☆270Updated 7 years ago
- Implementation of VLAE☆215Updated 7 years ago
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆572Updated 3 years ago
- A user-centered Python package for differentiable probabilistic inference☆202Updated 4 years ago
- Dropout As A Bayesian Approximation: Code☆201Updated 9 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆249Updated 9 months ago
- a python implementation of various versions of the information bottleneck, including automated parameter searching☆126Updated 4 years ago
- Collection of probabilistic models and inference algorithms☆241Updated 5 years ago
- Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation☆464Updated 6 years ago