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Found 5 papers, 1 papers with code
Evolutionary Variational Optimization of Generative Models
In general, our investigations highlight the importance of research on optimization methods for generative models to achieve performance improvements.
Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents
Discrete latent variables are considered important for real world data, which has motivated research on Variational Autoencoders (VAEs) with discrete latents.
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables
Here, we consider LVMs that are defined for a range of different distributions, i. e., observables can follow any (regular) distribution of the exponential family.
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding.
Evolutionary Expectation Maximization for Generative Models with Binary Latents
In general we believe that, with the link established here, standard as well as recent results in the field of evolutionary optimization can be leveraged to address the difficult problem of parameter optimization in generative models.
