ctallec / pyvarinfLinks
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
- Probabilistic Torch is library for deep generative models that extends PyTorch☆890Updated last year
- Papers for Bayesian-NN☆325Updated 6 years ago
- Understanding normalizing flows☆132Updated 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
- A PyTorch library for two-sample tests☆241Updated 2 years ago
- MADE (Masked Autoencoder Density Estimation) implementation in PyTorch☆557Updated 6 years ago
- Tools for loading standard data sets in machine learning☆204Updated 2 years ago
- Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation☆467Updated 6 years ago
- Implementing Bayes by Backprop☆184Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Learning kernels to maximize the power of MMD tests☆210Updated 7 years ago
- Code for the paper "VAE with a VampPrior", J.M. Tomczak & M. Welling☆228Updated 7 years ago
- Implementation of VLAE☆215Updated 7 years ago
- PyTorch implementations of algorithms for density estimation☆586Updated 4 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Updated 9 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆249Updated 11 months ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆140Updated 7 years ago
- Structured Inference Networks for Nonlinear State Space Models☆272Updated 7 years ago
- Replicating "Understanding disentangling in β-VAE"☆198Updated 7 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
- A structured list of resources about Sum-Product Networks (SPNs)☆254Updated 4 years ago
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆575Updated 3 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
- PyTorch implementation of "Weight Uncertainty in Neural Networks"☆176Updated 3 years ago
- Tensorflow implementation of Hyperspherical Variational Auto-Encoders☆232Updated 6 years ago
- Sparse Variational Dropout, ICML 2017☆313Updated 5 years ago