5 papers
Phase-Type Variational Autoencoders for Heavy-Tailed Data
Abdelhakim Ziani, András Horváth, Paolo Ballarini
Heavy-tailed distributions are ubiquitous in real-world data, where rare but extreme events dominate risk and variability. However, standard Variational Autoencoders (VAEs) employ…
Mitigating Pretraining-Induced Attention Asymmetry in 2D+ Electron Microscopy Image Segmentation
Zsófia Molnár, Gergely Szabó, András Horváth
Vision models pretrained on large-scale RGB natural image datasets are widely reused for electron microscopy image segmentation. In electron microscopy, volumetric data are acquire…
Approximating Heavy-Tailed Distributions with a Mixture of Bernstein Phase-Type and Hyperexponential Models
Abdelhakim Ziani, András Horváth, Paolo Ballarini
Heavy-tailed distributions, prevalent in a lot of real-world applications such as finance, telecommunications, queuing theory, and natural language processing, are challenging to m…
Statistical process discovery
Pierre Cry, Paolo Ballarini, András Horváth +1
Stochastic process discovery is concerned with deriving a model capable of reproducing the stochastic character of observed executions of a given process, stored in a log. This lea…
Probabilistic Process Discovery with Stochastic Process Trees
András Horváth, Paolo Ballarini, Pierre Cry
In order to obtain a stochastic model that accounts for the stochastic aspects of the dynamics of a business process, usually the following steps are taken. Given an event log, a p…