53 citations · 113 across the 16 of their papers we have counts for
17 papers
Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters
Sebastian Salwig, Till Kahlke, Florian Hirschberger +2
Gaussian Mixture Models (GMMs) range among the most frequently used models in machine learning. However, training large, general GMMs becomes computationally prohibitive for data s…
Learning Sparse Codes with Entropy-Based ELBOs
Dmytro Velychko, Simon Damm, Asja Fischer +1
Standard probabilistic sparse coding assumes a Laplace prior, a linear mapping from latents to observables, and Gaussian observable distributions. We here derive a solely entropy-b…
On the Equality of the ELBO to a Sum of Entropies at Stationary Points of Learning
Jörg Lücke, Jan Warnken
The variational lower bound (a.k.a. ELBO or free energy) is the central objective for many established as well as for many novel algorithms for unsupervised learning. Such algorith…
Evolutionary Variational Optimization of Generative Models
Jakob Drefs, Enrico Guiraud, Jörg Lücke
We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimization and evolutionary algorithms. The combination is realize…
Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents
Enrico Guiraud, Jakob Drefs, Jörg Lücke
Discrete latent variables are considered important for real world data, which has motivated research on Variational Autoencoders (VAEs) with discrete latents. However, standard VAE…
The ELBO of Variational Autoencoders Converges to a Sum of Three Entropies
Simon Damm, Dennis Forster, Dmytro Velychko +3
The central objective function of a variational autoencoder (VAE) is its variational lower bound (the ELBO). Here we show that for standard (i.e., Gaussian) VAEs the ELBO converges…