CollOR: Distributed collaborative offloading and routing for tasks with QoS demands in multi-robot system
Multi-access edge computing (MEC) offers prospective opportunities for robots that have
various computational tasks to execute in Industry 4.0, smart cities and many other fields …
various computational tasks to execute in Industry 4.0, smart cities and many other fields …
Implicit generative copulas
Copulas are a powerful tool for modeling multivariate distributions as they allow to
separately estimate the univariate marginal distributions and the joint dependency structure …
separately estimate the univariate marginal distributions and the joint dependency structure …
Multi-distribution mixture generative adversarial networks for fitting diverse data sets
There has been remarkable success in many areas with generative adversarial networks
(GAN). However, their performance is usually limited when they are trained on data sets with …
(GAN). However, their performance is usually limited when they are trained on data sets with …
Copula density neural estimation
Probability density estimation from observed data constitutes a central task in statistics.
Recent advancements in machine learning offer new tools but also pose new challenges …
Recent advancements in machine learning offer new tools but also pose new challenges …
Learning Nonparametric High-Dimensional Generative Models: The Empirical-Beta-Copula Autoencoder
By sampling from the latent space of an autoencoder and decoding the latent space
samples to the original data space, any autoencoder can simply be turned into a generative …
samples to the original data space, any autoencoder can simply be turned into a generative …
Can diffusion models capture extreme event statistics?
S Stamatelopoulos, TP Sapsis - Computer Methods in Applied Mechanics …, 2025 - Elsevier
For many important problems it is essential to be able to accurately quantify the statistics of
extremes for specific quantities of interest, such as extreme atmospheric weather events or …
extremes for specific quantities of interest, such as extreme atmospheric weather events or …
Deep into The Domain Shift: Transfer Learning through Dependence Regularization
Classical domain adaptation methods acquire transferability by regularizing the overall
distributional discrepancies between features in the source domain (labeled) and features in …
distributional discrepancies between features in the source domain (labeled) and features in …
Discriminative mutual information estimators for channel capacity learning
Channel capacity plays a crucial role in the development of modern communication systems
as it represents the maximum rate at which information can be reliably transmitted over a …
as it represents the maximum rate at which information can be reliably transmitted over a …
Combining deep generative models with extreme value theory for synthetic hazard simulation: a multivariate and spatially coherent approach
Climate hazards can cause major disasters when they occur simultaneously as compound
hazards. To understand the distribution of climate risk and inform adaptation policies …
hazards. To understand the distribution of climate risk and inform adaptation policies …
Capacity learning for communication systems over power lines
The development of power line communication (PLC) systems and algorithms is significantly
challenged by the presence of unconventional noise. The analytic description of the PLC …
challenged by the presence of unconventional noise. The analytic description of the PLC …