Kaspar Märtens

NeuralProcesses. Neural Processes implementation for 1D regression

BasisVAE. PyTorch implementation of BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders

c-GPLVM. PyTorch implementation of the Covariate-GPLVM

NeuralDecomposition. PyTorch implementation of the paper "Neural Decomposition: Functional ANOVA with Variational Autoencoders"

NUTS. HMC No-U-Turn sampler (NUTS) implementation in R

mixtureModels. MCMC for Dirichlet Process mixture models

shared-private-multimodalVAE. PyTorch implementation for shared-private Multimodal Variational Autoencoders

rarity. Implementarion of Rarity: Discovering rare cell populations from single-cell imaging data

y10k-prediction. Code for our paper: Predicting quantitative traits from genome and phenome with near perfect accuracy

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ensembleFHMM. R package for ensemble MCMC for Factorial Hidden Markov Models