Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Utrecht UniversityNL210 papers
- Delft University of TechnologyNL196 papers
- Centre National de la Recherche ScientifiqueFR195 papers
- Centrum Wiskunde & InformaticaNL163 papers
- University of TwenteNL158 papers
- University of AmsterdamNL113 papers
- Radboud University NijmegenNL89 papers
- University of Rome Tor VergataIT84 papers
- Vrije Universiteit AmsterdamNL79 papers
- Leiden UniversityNL72 papers
- University of GroningenNL58 papers
- Philips (Netherlands)NL56 papers
112 papers · 1 filter
Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation
Haseeb Tariq, Marwan Hassani
Money laundering presents a persistent challenge for financial institutions worldwide, while criminal organizations constantly evolve their tactics to bypass detection systems. Tra…
Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction
Marwan Hassani, Tamara Verbeek, Sjoerd van Straten
Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes…
Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems
Qusai Khaled, Uzay Kaymak, Laura Genga
Fuzzy systems show strong potential in explainable AI due to their rule-based architecture and linguistic variables. Existing approaches navigate the accuracy-explainability trade-…
LOREN: Low Rank-Based Code-Rate Adaptation in Neural Receivers
Bram Van Bolderik, Vlado Menkovski, Sonia Heemstra de Groot +1
Neural network based receivers have recently demonstrated superior system-level performance compared to traditional receivers. However, their practicality is limited by high memory…
Interpretable Fuzzy Systems For Forward Osmosis Desalination
Qusai Khaled, Uzay Kaymak, Laura Genga
Preserving interpretability in fuzzy rule-based systems (FRBS) is vital for water treatment, where decisions impact public health. While structural interpretability has been addres…
Towards Privacy-Aware Bayesian Networks: A Credal Approach
Niccolò Rocchi, Fabio Stella, Cassio de Campos
Bayesian networks (BN) are probabilistic graphical models that enable efficient knowledge representation and inference. These have proven effective across diverse domains, includin…
Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation
Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich
Dimensionality Reduction (DR) is widely used for visualizing high-dimensional data, often with the goal of revealing expected cluster structure. However, such a structure may not a…
Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation
Gregor Baer, Isel Grau, Chao Zhang +1
Evaluating feature attribution methods represents a critical challenge in explainable AI (XAI), as researchers typically rely on perturbation-based metrics when ground truth is una…
Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring
Sjoerd van Straten, Alessandro Padella, Marwan Hassani
Predictive Process Monitoring (PPM) enables forecasting future events or outcomes of ongoing business process instances based on event logs. However, deep learning PPM approaches a…
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models
J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
Reinforcement learning (RL) is a powerful framework for decision-making in uncertain environments, but it often requires large amounts of data to learn an optimal policy. We addres…
Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
Gregor Baer, Isel Grau, Chao Zhang +1
As machine learning models become increasingly prevalent in time series applications, Explainable Artificial Intelligence (XAI) methods are essential for understanding their predic…
Deep Variational Sequential Monte Carlo for High-Dimensional Observations
Wessel L. van Nierop, Nir Shlezinger, Ruud J. G. van Sloun
Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-…
DBSCAN in domains with periodic boundary conditions
Xander M. de Wit, Alessandro Gabbana
Many scientific problems involve data that is embedded in a space with periodic boundary conditions. This can for instance be related to an inherent cyclic or rotational symmetry i…
PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals
Saurav R. Pandey, Aaqib Saeed, Harlin Lee
Pediatric sleep is an important but often overlooked area in health informatics. We present PedSleepMAE, a generative model that fully leverages multimodal pediatric sleep signals…
Two-level deep domain decomposition method
Victorita Dolean, Serge Gratton, Alexander Heinlein +1
This study presents a two-level Deep Domain Decomposition Method (Deep-DDM) augmented with a coarse-level network for solving boundary value problems using physics-informed neural…
A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams
Ben Halstead, Yun Sing Koh, Patricia Riddle +2
The distribution of streaming data often changes over time as conditions change, a phenomenon known as concept drift. Only a subset of previous experience, collected in similar con…
Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning
Lisandro A. Jimenez-Roa, Thiago D. Simão, Zaharah Bukhsh +4
Large-scale infrastructure systems are crucial for societal welfare, and their effective management requires strategic forecasting and intervention methods that account for various…
Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
Mahmoud Ibrahim, Yasmina Al Khalil, Sina Amirrajab +6
This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic…
Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning
Ralf Raumanns, Gerard Schouten, Josien P. W. Pluim +1
The influence of bias in datasets on the fairness of model predictions is a topic of ongoing research in various fields. We evaluate the performance of skin lesion classification u…
Magnetic Hysteresis Modeling with Neural Operators
Abhishek Chandra, Bram Daniels, Mitrofan Curti +2
Hysteresis modeling is crucial to comprehend the behavior of magnetic devices, facilitating optimal designs. Hitherto, deep learning-based methods employed to model hysteresis, fac…
Probabilistic Inference in the Era of Tensor Networks and Differential Programming
Martin Roa-Villescas, Xuanzhao Gao, Sander Stuijk +2
Probabilistic inference is a fundamental task in modern machine learning. Recent advances in tensor network (TN) contraction algorithms have enabled the development of better exact…
Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability
Céline Comte, Matthieu Jonckheere, Jaron Sanders +1
In this paper, we introduce a policy-gradient method for model-based reinforcement learning (RL) that exploits a type of stationary distributions commonly obtained from Markov deci…
Node Classification in Random Trees
Wouter W. L. Nuijten, Vlado Menkovski
We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the…
Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions
Haseeb Tariq, Marwan Hassani
Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered a…