1 code implementation • 25 Mar 2024 • Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens
In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning.
1 code implementation • 18 Oct 2023 • Philipp Borchert, Jochen De Weerdt, Kristof Coussement, Arno De Caigny, Marie-Francine Moens
To evaluate the performance of state-of-the-art RC models on the CORE dataset, we conduct experiments in the few-shot domain adaptation setting.
1 code implementation • 16 Oct 2023 • Manon Reusens, Philipp Borchert, Margot Mieskes, Jochen De Weerdt, Bart Baesens
This paper investigates the transferability of debiasing techniques across different languages within multilingual models.
no code implementations • 7 Jun 2023 • Hans Weytjens, Wouter Verbeke, Jochen De Weerdt
Our contribution consists of experiments on timed process interventions with synthetic data that renders genuine online RL and the comparison to CI possible, and allows for an accurate evaluation of the results.
1 code implementation • 13 Dec 2022 • Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
In this work, we investigate the capabilities of such an LSTM to actually learn the underlying process model structure of an event log.
no code implementations • 16 Aug 2022 • Galina Deeva, Johannes De Smedt, Cecilia Saint-Pierre, Richard Weber, Jochen De Weerdt
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula.
1 code implementation • 27 Jun 2022 • Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn, Jochen De Weerdt
For realising timely delivery, an accurate prediction of the remaining time of the delivery process is crucial.
1 code implementation • 13 Jun 2022 • Hans Weytjens, Jochen De Weerdt
The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use.
1 code implementation • 24 Feb 2022 • Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Therefore, in this work, we propose an evaluation scheme complemented with new fitness, precision, and generalisation metrics, specifically tailored towards measuring the capacity of deep learning models to learn process model structure.
no code implementations • 13 Oct 2021 • Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups.
no code implementations • 5 Jul 2021 • Hans Weytjens, Jochen De Weerdt
Often the training and test sets are not completely separated, a data leakage problem particular to predictive process monitoring.
1 code implementation • 12 May 2021 • Hans Weytjens, Jochen De Weerdt
This obliviousness of uncertainty is a major obstacle towards their adoption in practice.
1 code implementation • 3 May 2021 • Johannes De Smedt, Anton Yeshchenko, Artem Polyvyanyy, Jochen De Weerdt, Jan Mendling
To this end, we develop a technique to forecast the entire process model from historical event data.
no code implementations • 14 Apr 2021 • Hans Weytjens, Jochen De Weerdt
Attention is another technique that, in combination with LSTMs, has found application in time series classification and was included in our research.
no code implementations • 5 Nov 2020 • Johannes De Smedt, Jochen De Weerdt
To achieve this, Processes-As-Movies (PAM) is introduced, i. e., a novel technique capable of jointly mining and predicting declarative process constraints between activities in various windows of a process' execution.