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pro vyhledávání: '"A, de Vos"'
The goal of uplift modeling is to recommend actions that optimize specific outcomes by determining which entities should receive treatment. One common approach involves two steps: first, an inference step that estimates conditional average treatment
Externí odkaz:
http://arxiv.org/abs/2412.09232
Autor:
Peeperkorn, Jari, De Vos, Simon
Predictive process monitoring focuses on forecasting future states of ongoing process executions, such as predicting the outcome of a particular case. In recent years, the application of machine learning models in this domain has garnered significant
Externí odkaz:
http://arxiv.org/abs/2412.04914
Autor:
Kontras, Konstantinos, Strypsteen, Thomas, Chatzichristos, Christos, Liang, Paul Pu, Blaschko, Matthew, De Vos, Maarten
Multimodal learning can complete the picture of information extraction by uncovering key dependencies between data sources. However, current systems fail to fully leverage multiple modalities for optimal performance. This has been attributed to modal
Externí odkaz:
http://arxiv.org/abs/2411.07335
To investigate how the radio-identified active galactic nuclei (AGN) fraction varies with cluster-centric radius, we present the projected and de-projected distributions of a large sample of LOFAR-identified radio AGN out to $30R_{500}$ around galaxy
Externí odkaz:
http://arxiv.org/abs/2410.13440
Autor:
Biswas, Sayan, Kermarrec, Anne-Marie, Marouani, Alexis, Pires, Rafael, Sharma, Rishi, De Vos, Martijn
Decentralized learning (DL) is an emerging technique that allows nodes on the web to collaboratively train machine learning models without sharing raw data. Dealing with stragglers, i.e., nodes with slower compute or communication than others, is a k
Externí odkaz:
http://arxiv.org/abs/2410.12918
Autor:
de Vos, Koen, Torta, Elena, Bruyninckx, Herman, Martinez, Cesar Lopez, van de Molengraft, Rene
This paper presents a framework for multi-agent navigation in structured but dynamic environments, integrating three key components: a shared semantic map encoding metric and semantic environmental knowledge, a claim policy for coordinating access to
Externí odkaz:
http://arxiv.org/abs/2410.12651
Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level o
Externí odkaz:
http://arxiv.org/abs/2410.02541
Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an extension to the N-BEATS model for univariate time series point forecast
Externí odkaz:
http://arxiv.org/abs/2409.18267
Autor:
Albert, Julien, Balfroid, Martin, Doh, Miriam, Bogaert, Jeremie, La Fisca, Luca, De Vos, Liesbet, Renard, Bryan, Stragier, Vincent, Jean, Emmanuel
Recommender systems have become integral to our digital experiences, from online shopping to streaming platforms. Still, the rationale behind their suggestions often remains opaque to users. While some systems employ a graph-based approach, offering
Externí odkaz:
http://arxiv.org/abs/2409.06297
Autor:
Autthasan, Phairot, Chaisaen, Rattanaphon, Phan, Huy, De Vos, Maarten, Wilaiprasitporn, Theerawit
Publikováno v:
IEEE Internet of Things Journal 2024
Recent advances in deep learning (DL) have significantly impacted motor imagery (MI)-based brain-computer interface (BCI) systems, enhancing the decoding of electroencephalography (EEG) signals. However, most studies struggle to identify discriminati
Externí odkaz:
http://arxiv.org/abs/2409.04104