Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.
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Updated
Jun 11, 2024 - Jupyter Notebook
Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.
This repository contains Python (Jupyter Notebooks), C and Shell code, which was used to generate figures in a paper under the same name.
This repository contains the code for the analysis reported in Physical Review E 96, 022417.
Codes and instructions to replicate the research published in Cobarrubia et al. Frontiers in Physics 2021.
Exploration of voter model with power law time-dependent event rates
3D Slicer extension that provides several approaches in order to apply the anomalous spatial filters on medical images.
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