Zobrazeno 1 - 10
of 182
pro vyhledávání: '"De Weerdt, Jochen"'
Large Language Models (LLMs) excel at providing information acquired during pretraining on large-scale corpora and following instructions through user prompts. This study investigates whether the quality of LLM responses varies depending on the demog
Externí odkaz:
http://arxiv.org/abs/2406.17385
Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data extract rich information spanning the domain, entities, and relations
Externí odkaz:
http://arxiv.org/abs/2403.16543
Autor:
Borchert, Philipp, De Weerdt, Jochen, Coussement, Kristof, De Caigny, Arno, Moens, Marie-Francine
We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. CORE includes 4,708 instances of 12 relation types with corresponding textual evidence extracted from company Wikipedia pages.
Externí odkaz:
http://arxiv.org/abs/2310.12024
Autor:
Adams, Jan Niklas, Peeperkorn, Jari, Brockhoff, Tobias, Terrier, Isabelle, Göhner, Heiko, Uysal, Merih Seran, Broucke, Seppe vanden, De Weerdt, Jochen, van der Aalst, Wil M. P.
Process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multiple permutations, increasing data requirements, especially for processes
Externí odkaz:
http://arxiv.org/abs/2310.11332
This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we dem
Externí odkaz:
http://arxiv.org/abs/2310.10310
The shift from the understanding and prediction of processes to their optimization offers great benefits to businesses and other organizations. Precisely timed process interventions are the cornerstones of effective optimization. Prescriptive process
Externí odkaz:
http://arxiv.org/abs/2306.04299
Publikováno v:
Journal of Intelligent Information Systems 2022
Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely next event or suffix, its remaining time, or an outcome-related variab
Externí odkaz:
http://arxiv.org/abs/2212.06430
Publikováno v:
Expert Systems with Applications, 118182 (2022)
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity
Externí odkaz:
http://arxiv.org/abs/2208.07749
Reliable remaining time prediction of ongoing business processes is a highly relevant topic. One example is order delivery, a key competitive factor in e.g. retailing as it is a main driver of customer satisfaction. For realising timely delivery, an
Externí odkaz:
http://arxiv.org/abs/2206.13109
Autor:
Weytjens, Hans, De Weerdt, Jochen
Publikováno v:
Applied Soft Computing (2022), 109134
The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use. We distinguish two types of learnable uncertainty: model uncertainty due to a lack of training data and noise-induced
Externí odkaz:
http://arxiv.org/abs/2206.06317