Learning Reversible Symplectic Dynamics. - Riccardo Valperga; Kevin Webster; Dmitry Turaev; Victoria Klein; Jeroen S. W. Lamb (L4DC, 2022)

Geometric Contrastive Learning. - Yeskendir Koishekenov; Sharvaree P. Vadgama; Riccardo Valperga; Erik J. Bekkers (ICCV (Workshops), 2023)

Learning Lie Group Symmetry Transformations with Neural Networks. - Alex Gabel; Victoria Klein; Riccardo Valperga; Jeroen S. W. Lamb; Kevin Webster; Rick Quax; Efstratios Gavves (TAG-ML, 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)

Data Augmentations in Deep Weight Spaces. - Aviv Shamsian; David W. Zhang; Aviv Navon; Yan Zhang; Miltiadis Kofinas; Idan Achituve; Riccardo Valperga; Gertjan J. Burghouts; Efstratios Gavves; Cees G. M. Snoek; Ethan Fetaya; Gal Chechik; et al. (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)

Ai-sampler: Adversarial Learning of Markov kernels with involutive maps. - Evgenii Egorov; Riccardo Valperga; Stratis Gavves (ICML, 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)

Ai-Sampler: Adversarial Learning of Markov kernels with involutive maps. - Evgenii Egorov; Riccardo Valperga; Efstratios Gavves (CoRR, 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)