Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups. - David M. Knigge; David W. Romero; Erik J. Bekkers (ICML, 2022)

Learning to Automatically Generate Accurate ECG Captions. - Mathieu G. G. Bartels; Ivona Najdenkoska; Rutger R. van de Leur; Arjan Sammani; Karim Taha; David M. Knigge; Pieter A. F. M. Doevendans; Marcel Worring; René van Es (MIDL, 2022)

Towards a General Purpose CNN for Long Range Dependencies in ND. - David W. Romero; David M. Knigge; Albert Gu; Erik J. Bekkers; Efstratios Gavves; Jakub M. Tomczak; Mark Hoogendoorn (CoRR, 2022)

Geometric Superpixel Representations for Efficient Image Classification with Graph Neural Networks. - Radu A. Cosma; Lukas Knobel; Putri A. van der Linden; David M. Knigge; Erik J. Bekkers (ICCV (Workshops), 2023)

Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN. - David M. Knigge; David W. Romero; Albert Gu; Efstratios Gavves; Erik J. Bekkers; Jakub Mikolaj Tomczak; Mark Hoogendoorn; Jan-Jakob Sonke (ICLR, 2023)

Neural Modulation Fields for Conditional Cone Beam Neural Tomography. - Samuele Papa; David M. Knigge; Riccardo Valperga; Nikita Moriakov; Miltos Kofinas; Jan-Jakob Sonke; Efstratios Gavves (CoRR, 2023)

How to Train Neural Field Representations: A Comprehensive Study and Benchmark. - Samuele Papa; Riccardo Valperga; David M. Knigge; Miltiadis Kofinas; Phillip Lippe; Jan-Jakob Sonke; Efstratios Gavves (CVPR, 2024)

Space-Time Continuous PDE Forecasting using Equivariant Neural Fields. - David M. Knigge; David R. Wessels; Riccardo Valperga; Samuele Papa; Jan-Jakob Sonke; Erik J. Bekkers; Efstratios Gavves (NeurIPS, 2024)

Grounding Continuous Representations in Geometry: Equivariant Neural Fields. - David R. Wessels; David M. Knigge; Riccardo Valperga; Samuele Papa; Sharvaree P. Vadgama; Efstratios Gavves; Erik J. Bekkers (ICLR, 2025)