Deep probabilistic analysis of single-cell and spatial omics data
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Updated
May 13, 2024 - Python
Deep probabilistic analysis of single-cell and spatial omics data
High-performance reactive message-passing based Bayesian inference engine
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach, AISTATS 2024.
Variational Joint Filtering
Bayesian neural networks in PyTorch
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
This repository is for sharing the scripts of EM algorithm and variational bayes.
Model for learning document embeddings along with their uncertainties
Clustering with variational Bayes and population Monte Carlo
PyTorch implementation of a variational autoencoder (VAE) for use on multi-channel 2D data such as images
Expectation Maximisation, Variational Bayes, ARD, Loopy Belief Propagation, Gaussian Process Regression
A toolbox for inference of mixture models
Code for keynote talk at Nonlinear System Identification Workshop 2023 at TU Eindhoven.
Implementation and derivation of "Variational Bayesian inference for a nonlinear forward model." [Chappell et al. 2008] for arbitrary, user-defined model errors.
A simple library to run variational inference on Stan models.
A study on the following problems: what the memorization problem is in meta-learning; why memorization problem happens; and how we can prevent it. (ICLR 2020)
Algorithms for inference in Gaussian Mixture Models.
Inference in the Bayesian Latent Dirichlet Allocation (LDA) using Gibbs Sampling and Variational Bayes
Code and experiments for the preprint: "Bayesian Neural Network Versus Ex-Post Calibration For Capturing Prediction Uncertainty".
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