3 citations · 3 across the 4 of their papers we have counts for
4 papers
Latent variable estimation with composite Hilbert space Gaussian processes
Soham Mukherjee, Javier Enrique Aguilar, Marcello Zago +2
We develop a scalable class of models for latent variable estimation using composite Gaussian processes, with a focus on derivative Gaussian processes. We jointly model multiple da…
Hilbert space methods for approximating multi-output latent variable Gaussian processes
Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner
Gaussian processes are a powerful class of non-linear models, but have limited applicability for larger datasets due to their high computational complexity. In such cases, approxim…
DGP-LVM: Derivative Gaussian process latent variable models
Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner
We develop a framework for derivative Gaussian process latent variable models (DGP-LVMs) that can handle multi-dimensional output data using modified derivative covariance function…
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath +1
Clustering high-dimensional data, such as images or biological measurements, is a long-standingproblem and has been studied extensively. Recently, Deep Clustering has gained popula…