Bouligand Derivatives and Robustness of Support Vector Machines for Regression. - Andreas Christmann; Arnout Van Messem (J. Mach. Learn. Res., 2008)

A review on consistency and robustness properties of support vector machines for heavy-tailed distributions. - Arnout Van Messem; Andreas Christmann (Adv. Data Anal. Classif., 2010)

How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples. - Utku Ozbulak; Wesley De Neve; Arnout Van Messem (CoRR, 2018)

Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding. - Utku Ozbulak; Arnout Van Messem; Wesley De Neve (IJCNN, 2019)

Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation. - Utku Ozbulak; Arnout Van Messem; Wesley De Neve (MICCAI (2), 2019)

Developing a Segmentation Model for Microscopic Images of Microplastics Isolated from Clams. - Ji Yeon Baek; Maria Krishna de Guzman; Ho-min Park; Sanghyeon Park; Boyeon Shin; Tanja Cirkovic Velickovic; Arnout Van Messem; Wesley De Neve (ICPR Workshops (6), 2020)

3-D Deep Learning-Based Item Classification for Belt Conveyors Targeting Packaging and Logistics. - Ho-min Park; Byungkon Kang; Arnout Van Messem; Wesley De Neve (ICPR Workshops (4), 2020)

Automatic Detection of Trypanosomosis in Thick Blood Smears Using Image Pre-processing and Deep Learning. - Taewoo Jung; Esla Timothy Anzaku; Utku Özbulak; Stefan Magez; Arnout Van Messem; Wesley De Neve (IHCI (2), 2020)

Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability. - Utku Ozbulak; Jonathan Peck; Wesley De Neve; Bart Goossens; Yvan Saeys; Arnout Van Messem (CoRR, 2020)

Perturbation analysis of gradient-based adversarial attacks. - Utku Ozbulak; Manvel Gasparyan; Wesley De Neve; Arnout Van Messem (Pattern Recognit. Lett., 2020)

An Automated Model Reduction Method for Biochemical Reaction Networks. - Manvel Gasparyan; Arnout Van Messem; Shodhan Rao (Symmetry, 2020)

Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks. - Utku Ozbulak; Esla Timothy Anzaku; Wesley De Neve; Arnout Van Messem (BMVC, 2021)

Towards a quantitative analysis of class activation mapping for deep learning-based computer-aided diagnosis. - Hanul Kang; Ho-min Park; Yuju Ahn; Arnout Van Messem; Wesley De Neve (Image Perception, Observer Performance, and Technology Assessment, 2021)

Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes. - Utku Ozbulak; Maura Pintor; Arnout Van Messem; Wesley De Neve (CoRR, 2021)

Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems. - Utku Ozbulak; Baptist Vandersmissen; Azarakhsh Jalalvand; Ivo Couckuyt; Arnout Van Messem; Wesley De Neve (Comput. Vis. Image Underst., 2021)

Exact Feature Collisions in Neural Networks. - Utku Ozbulak; Manvel Gasparyan; Shodhan Rao; Wesley De Neve; Arnout Van Messem (CoRR, 2022)

A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets. - Esla Timothy Anzaku; Haohan Wang; Arnout Van Messem; Wesley De Neve (CoRR, 2022)

Leveraging Human-Machine Interactions for Computer Vision Dataset Quality Enhancement. - Esla Timothy Anzaku; Hyesoo Hong; Jin-Woo Park; Wonjun Yang; Kangmin Kim; Jongbum Won; Deshika Vinoshani Kumari Herath; Arnout Van Messem; Wesley De Neve (IHCI (1), 2023)

MuSe-Personalization 2023: Feature Engineering, Hyperparameter Optimization, and Transformer-Encoder Re-discovery. - Ho-min Park; Ganghyun Kim; Arnout Van Messem; Wesley De Neve (MuSe@ACM Multimedia, 2023)

Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training. - Utku Ozbulak; Hyun Jung Lee; Beril Boga; Esla Timothy Anzaku; Ho-min Park; Arnout Van Messem; Wesley De Neve; Joris Vankerschaver (Trans. Mach. Learn. Res., 2023)

Parameter estimation for models of chemical reaction networks from experimental data of reaction rates. - Manvel Gasparyan; Arnout Van Messem; Shodhan Rao (Int. J. Control, 2023)

CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins. - Ho-min Park; Jongbum Won; Yunseol Park; Esla Timothy Anzaku; Joris Vankerschaver; Arnout Van Messem; Wesley De Neve; Hyunjin Shim (BMC Bioinform., 2023)

Grasshopper Optimization Algorithm (GOA): A Novel Algorithm or A Variant of PSO? - Negin Harandi; Arnout Van Messem; Wesley De Neve; Joris Vankerschaver (ANTS, 2024)

Re-assessing ImageNet: How aligned is its single-label assumption with its multi-label nature? - Esla Timothy Anzaku; Seyed Amir Mousavi; Arnout Van Messem; Wesley De Neve (CoRR, 2024)

Interpreting Stress Detection Models Using SHAP and Attention for MuSe-Stress 2022. - Ho-min Park; Ganghyun Kim; Jinsung Oh; Arnout Van Messem; Wesley De Neve (IEEE Trans. Affect. Comput., 2025)

Re-assessing accuracy degradation: a framework for understanding DNN behavior on similar-but-non-identical test datasets. - Esla Timothy Anzaku; Haohan Wang; Ajiboye Babalola; Arnout Van Messem; Wesley De Neve (Mach. Learn., 2025)