ctallec / pyvarinfLinks
Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
☆361Updated 6 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☆254Updated 7 years ago
- A PyTorch library for two-sample tests☆242Updated 2 years ago
- Probabilistic Torch is library for deep generative models that extends PyTorch☆892Updated last year
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 7 years ago
- Papers for Bayesian-NN☆326Updated 6 years ago
- Implementing Bayes by Backprop☆184Updated 6 years ago
- Understanding normalizing flows☆132Updated 5 years ago
- Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation☆469Updated 6 years ago
- Active Learning on Image Data using Bayesian ConvNets☆139Updated 9 years ago
- Tools for loading standard data sets in machine learning☆205Updated 3 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆141Updated 7 years ago
- This repository contains the code to reproduce the core results from the paper "Adversarial Variational Bayes: Unifying Variational Autoe…☆208Updated 7 years ago
- MADE (Masked Autoencoder Density Estimation) implementation in PyTorch☆565Updated 6 years ago
- Code for the paper "VAE with a VampPrior", J.M. Tomczak & M. Welling☆234Updated 7 years ago
- PyTorch implementation of "Weight Uncertainty in Neural Networks"☆176Updated 3 years ago
- Implementation of VLAE☆216Updated 7 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago
- PyTorch implementation of Neural Processes☆88Updated 6 years ago
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆574Updated 3 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- PyTorch implementations of algorithms for density estimation☆585Updated 4 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 8 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 6 years ago
- Learning kernels to maximize the power of MMD tests☆211Updated 7 years ago
- A structured list of resources about Sum-Product Networks (SPNs)☆255Updated 4 years ago
- Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"☆244Updated 5 years ago
- Gradient based hyperparameter optimization & meta-learning package for TensorFlow☆190Updated 5 years ago
- Code for some of the experiments I did with variational autoencoders on multi-modality and atari video prediction. Atari video prediction…☆62Updated 9 years ago
- ☆110Updated 8 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago