64 citations · 239 across the 24 of their papers we have counts for
4 papers · 1 filter
Training a Restricted Boltzmann Machine for Classification by Labeling Model Samples
Malte Probst, Franz Rothlauf
We propose an alternative method for training a classification model. Using the MNIST set of handwritten digits and Restricted Boltzmann Machines, it is possible to reach a classif…
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization
Malte Probst
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Generative Adversarial Networks (GAN) are generative n…
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial Optimization
Malte Probst, Franz Rothlauf
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Deep Boltzmann Machines (DBMs) are generative neural n…
Denoising Autoencoders for fast Combinatorial Black Box Optimization
Malte Probst
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks w…