Deep probabilistic analysis of single-cell and spatial omics data
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Updated
Jun 9, 2024 - Python
Deep probabilistic analysis of single-cell and spatial omics data
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
Tensorflow Implementation of Knowledge-Guided CVAE for dialog generation ACL 2017. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
MLSS2019 Tutorial on Bayesian Deep Learning
Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.
An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.
Official Pytorch code for (AAAI 2020) paper "Capsule Routing via Variational Bayes", https://arxiv.org/pdf/1905.11455.pdf
Variational Bayes linear and logistic regression
High-performance reactive message-passing based Bayesian inference engine
Scalable sparse Bayesian learning for large CS recovery problems
Single-cell Hierarchical Poisson Factorization
Clustering with variational Bayes and population Monte Carlo
Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
variational Bayesian algorithm for Brain MR image Segmentation
Dimensionality reduction of spikes trains
Model for learning document embeddings along with their uncertainties
A toolbox for inference of mixture models
Scala embedded universal probabilistic programming language
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)
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