Tropical Limits of Probability Spaces, Part I: The Intrinsic Kolmogorov-Sinai Distance and the Asymptotic Equipartition Property for Configurations. - Rostislav Matveev; Jacobus W. Portegies (CoRR, 2017)

SciSports: Learning football kinematics through two-dimensional tracking data. - Anatoliy Babic; Harshit Bansal; Gianluca Finocchio; Julian Golak; Mark A. Peletier; Jim Portegies; Clara Stegehuis; Anuj Tyagi; Roland Vincze; William Weimin Yoo (CoRR, 2018)

Total Variation and Mean Curvature PDEs on the Space of Positions and Orientations. - Remco Duits; Etienne St-Onge; Jim Portegies; Bart M. N. Smets (SSVM, 2019)

Tropical diagrams of probability spaces. - Rostislav Matveev; Jacobus W. Portegies (CoRR, 2019)

Diffusion Variational Autoencoders. - Luis A. Pérez Rey; Vlado Menkovski; Jim Portegies (IJCAI, 2020)

On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control. - Maurice Poot; Jim Portegies; Tom Oomen (CoRR, 2020)

Quantifying and Learning Disentangled Representations with Limited Supervision. - Loek Tonnaer; Luis A. Pérez Rey; Vlado Menkovski; Mike Holenderski; Jacobus W. Portegies (CoRR, 2020)

A Metric for Linear Symmetry-Based Disentanglement. - Luis A. Pérez Rey; Loek Tonnaer; Vlado Menkovski; Mike Holenderski; Jacobus W. Portegies (CoRR, 2020)

Tropical probability theory and an application to the entropic cone. - Rostislav Matveev; Jacobus W. Portegies (Kybernetika, 2020)

Kernel-Based Learning Control for Iteration-Varying Tasks Applied to a Printer With Friction. - Maurice Poot; Jim Portegies; Tom Oomen (AIM, 2021)

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder. - Max van Haren; Maurice Poot; Dragan Kostic; Robin van Es; Jim Portegies; Tom Oomen (AMC, 2021)

Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations. - Remco Duits; Bart M. N. Smets; Erik J. Bekkers; Jim Portegies (SSVM, 2021)

Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction. - Max van Meer; Maurice Poot; Jim Portegies; Tom Oomen (CoRR, 2021)

Total Variation and Mean Curvature PDEs on the Homogeneous Space of Positions and Orientations. - Bart M. N. Smets; Jim Portegies; Etienne St-Onge; Remco Duits (J. Math. Imaging Vis., 2021)

Position-Dependent Snap Feedforward: A Gaussian Process Framework. - Max van Haren; Maurice Poot; Jim Portegies; Tom Oomen (ACC, 2022)

Gaussian Processes for Advanced Motion Control*. - Maurice Poot; Jim Portegies; Noud Mooren; Max van Haren; Max van Meer; Tom Oomen (ACC, 2022)

Quantifying and Learning Linear Symmetry-Based Disentanglement. - Loek Tonnaer; Luis Armando Pérez Rey; Vlado Menkovski; Mike Holenderski; Jim Portegies (ICML, 2022)

Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi. - Bram Grooten; Jelle Wemmenhove; Maurice Poot; Jim Portegies (CoRR, 2022)

Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach. - Jilles van Hulst; Maurice Poot; Dragan Kostic; Kai Wa Yan; Jim Portegies; Tom Oomen (CoRR, 2022)

Universal Approximation in Dropout Neural Networks. - Oxana A. Manita; Mark A. Peletier; Jacobus W. Portegies; Jaron Sanders; Albert Senen-Cerda (J. Mach. Learn. Res., 2022)

Rational Basis Functions in Iterative Learning Control for Multivariable Systems. - Maurice Poot; Jim Portegies; Dragan Kostic; Tom Oomen (CDC, 2023)

Classification of Covering Spaces and Canonical Change of Basepoint. - Jelle Wemmenhove; Cosmin Manea; Jim Portegies (TYPES, 2023)

Waterproof: Educational Software for Learning How to Write Mathematical Proofs. - Jelle Wemmenhove; Dick Arends; Thijs Beurskens; Maitreyee Bhaid; Sean McCarren; Jan Moraal; Diego Rivera Garrido; David Tuin; Malcolm Vassallo; Pieter Wils; Jim Portegies (ThEdu@CADE, 2023)

PDE-Based Group Equivariant Convolutional Neural Networks. - Bart M. N. Smets; Jim Portegies; Erik J. Bekkers; Remco Duits (J. Math. Imaging Vis., 2023)

Arrow Contraction and Expansion in Tropical Diagrams. - Rostislav Matveev; Jacobus W. Portegies (Entropy, 2023)

Conditioning in Tropical Probability Theory. - Rostislav Matveev; Jacobus W. Portegies (Entropy, 2023)

Topological degree as a discrete diagnostic for disentanglement, with applications to the ΔVAE⋆. - Mahefa Ratsisetraina Ravelonanosy; Vlado Menkovski; Jacobus W. Portegies (DS (LB), 2024)

Neural Langevin Dynamics: Towards Interpretable Neural Stochastic Differential Equations. - Simon Martinus Koop; Mark A. Peletier; Jacobus Willem Portegies; Vlado Menkovski (NLDL, 2024)

Semiring Activation in Neural Networks. - Bart M. N. Smets; Peter D. Donker; Jim Portegies; Remco Duits (CoRR, 2024)

Formalization of Classification of Covering Spaces and Canonical Change of Basepoint in HoTT. - Jelle Wemmenhove; Cosmin Manea; Jim Portegies (DROPS Artifacts, 2024)

Position-Dependent Motion Feedforward via Gaussian Processes: Applied to Snap and Force Ripple in Semiconductor Equipment. - Maurice Poot; Max van Haren; Dragan Kostic; Jim Portegies; Tom Oomen (IEEE Trans. Control. Syst. Technol., 2024)

Service Design for the Improvement of Intelligent Tutoring Systems: A Case Study. - Jelle Wemmenhove; Dorina Bór; Rianne Conijn; Jim Portegies (J. Comput. Assist. Learn., 2025)

Experimentally-efficient data-driven rational feedforward control for multivariable systems. - Maurice Poot; Jim Portegies; Dragan Kostic; Tom Oomen (Int. J. Control, 2025)