4 citations · 7 across the 4 of their papers we have counts for
4 papers
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…
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables
Hamid Mousavi, Jakob Drefs, Florian Hirschberger +1
Latent variable models (LVMs) represent observed variables by parameterized functions of latent variables. Prominent examples of LVMs for unsupervised learning are probabilistic PC…
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
Georgios Exarchakis, Jörg Bornschein, Abdul-Saboor Sheikh +4
ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented algorithms seek to learn the elementary component…