Zobrazeno 1 - 10
of 1 656
pro vyhledávání: '"Nowaczyk, P."'
Autor:
Rahat, Mahmoud, Mashhadi, Peyman Sheikholharam, Nowaczyk, Sławomir, Choudhury, Shamik, Petrin, Leo, Rognvaldsson, Thorsteinn, Voskou, Andreas, Metta, Carlo, Savelli, Claudio
This paper presents an overview of the Volvo Discovery Challenge, held during the ECML-PKDD 2024 conference. The challenge's goal was to predict the failure risk of an anonymized component in Volvo trucks using a newly published dataset. The test dat
Externí odkaz:
http://arxiv.org/abs/2409.11446
Autor:
Nadolny, Jakub, Michałowski, Michał J., Parente, Massimiliano, Hjorth, Jens, Gall, Christa, Leśniewska, Aleksandra, Solar, Martín, Nowaczyk, Przemysław, Ryzhov, Oleh
Publikováno v:
A&A 689, A210 (2024)
Removing cold interstellar medium (ISM) from a galaxy is central to quenching star formation. However, the exact mechanism of this process remains unclear. The objective of this work is to find the mechanism responsible for dust and gas removal in si
Externí odkaz:
http://arxiv.org/abs/2406.16533
Unsupervised meta-learning aims to learn feature representations from unsupervised datasets that can transfer to downstream tasks with limited labeled data. In this paper, we propose a novel approach to unsupervised meta-learning that leverages the g
Externí odkaz:
http://arxiv.org/abs/2405.16124
Exploring the missing values is an essential but challenging issue due to the complex latent spatio-temporal correlation and dynamic nature of time series. Owing to the outstanding performance in dealing with structure learning potentials, Graph Neur
Externí odkaz:
http://arxiv.org/abs/2405.10995
Autor:
Altarabichi, Mohammed Ghaith, Nowaczyk, Sławomir, Pashami, Sepideh, Mashhadi, Peyman Sheikholharam
Publikováno v:
Expert Systems with Applications, 211, p.118528 (2023)
Evolutionary Algorithms (EAs) are often challenging to apply in real-world settings since evolutionary computations involve a large number of evaluations of a typically expensive fitness function. For example, an evaluation could involve training a n
Externí odkaz:
http://arxiv.org/abs/2404.03996
Autor:
Altarabichi, Mohammed Ghaith, Nowaczyk, Sławomir, Pashami, Sepideh, Mashhadi, Peyman Sheikholharam, Handl, Julia
Publikováno v:
Information Sciences, p.120500 (2024)
This paper investigates how various randomization techniques impact Deep Neural Networks (DNNs). Randomization, like weight noise and dropout, aids in reducing overfitting and enhancing generalization, but their interactions are poorly understood. Th
Externí odkaz:
http://arxiv.org/abs/2404.03992
Autor:
Abuella, Mohamed, Fanaee, Hadi, Atou, M. Amine, Nowaczyk, Slawomir, Johansson, Simon, Faghani, Ethan
To meet the urgent requirements for the climate change mitigation, several proactive measures of energy efficiency have been implemented in maritime industry. Many of these practices depend highly on the onboard data of vessel's operation and environ
Externí odkaz:
http://arxiv.org/abs/2404.00902
Publikováno v:
IEEE ACCESS, 2024
This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only positio
Externí odkaz:
http://arxiv.org/abs/2403.05778
Several approaches have been developed for improving the ship energy efficiency, thereby reducing operating costs and ensuring compliance with climate change mitigation regulations. Many of these approaches will heavily depend on measured data from o
Externí odkaz:
http://arxiv.org/abs/2402.00698
This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on privacy-preserving machine learning and underscores the need for tailo
Externí odkaz:
http://arxiv.org/abs/2310.00221