Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
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Updated
Apr 19, 2024 - Python
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
Deep probabilistic analysis of single-cell and spatial omics data
Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.
Official Pytorch code for (AAAI 2020) paper "Capsule Routing via Variational Bayes", https://arxiv.org/pdf/1905.11455.pdf
Single-cell Hierarchical Poisson Factorization
Clustering with variational Bayes and population Monte Carlo
Dimensionality reduction of spikes trains
Model for learning document embeddings along with their uncertainties
Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
A toolbox for inference of mixture models
A simple library to run variational inference on Stan models.
This repository is for sharing the scripts of EM algorithm and variational bayes.
Variational Joint Filtering
The sparse Bayesian learning sandbox
JAX version of vLGP (github.com/catniplab/vlgp)
Implementation and derivation of "Variational Bayesian inference for a nonlinear forward model." [Chappell et al. 2008] for arbitrary, user-defined model errors.
implement machine learning models from scratch
Cross-center smoothness prior for Bayesian image segmentation
An implementation of Stochastic Gradient Variational Bayes (SGVB) in PyTorch
Code and experiments for the preprint: "Bayesian Neural Network Versus Ex-Post Calibration For Capturing Prediction Uncertainty".
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