Alonso Urbano

Preprints

1. Urbano, A., Romero, D. W., Zimmer, M., and Pokutta, S. (2026). Neural Field Tokenizations with Hierarchy and Spatial Locality Priors. [arXiv]
   [BibTeX]

   @misc{2026_UrbanoRomeroZimmerPokutta_Neuralfieldtokenization_2606-08204,
     archiveprefix = {arXiv},
     eprint = {2606.08204},
     arxiv = {arXiv:2606.08204},
     primaryclass = {cs.LG},
     year = {2026},
     author = {Urbano, Alonso and Romero, David W. and Zimmer, Max and Pokutta, Sebastian},
     title = {Neural Field Tokenizations with Hierarchy and Spatial Locality Priors},
     date = {2026-06-06}
   }

Conference proceedings

1. Urbano, A., Romero, D. W., Zimmer, M., and Pokutta, S. (2026, January). RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization. Proceedings of the International Conference on Learning Representations. [arXiv]
   [BibTeX]

   @inproceedings{2025_UrbanoWZimmerPokutta_Reconsymmetrydiscovery,
     year = {2026},
     booktitle = {Proceedings of the International Conference on Learning Representations},
     month = jan,
     archiveprefix = {arXiv},
     eprint = {2505.13289},
     arxiv = {arXiv:2505.13289},
     primaryclass = {cs.LG},
     author = {Urbano, Alonso and Romero, David W. and Zimmer, Max and Pokutta, Sebastian},
     title = {RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization}
   }

2. Urbano, A., and Romero, D. W. (2024). Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries. Proceedings of the Geometry-Grounded Representation Learning and Generative Modeling Workshop (GRaM) @ ICML 2024. [arXiv]
   [BibTeX]

   @inproceedings{2023_UrbanoRomero_Selfsupervisedsymmetries,
     year = {2024},
     booktitle = {Proceedings of the Geometry-Grounded Representation Learning and Generative Modeling Workshop (GRaM) @ ICML 2024},
     archiveprefix = {arXiv},
     eprint = {2312.12223},
     arxiv = {arXiv:2312.12223},
     primaryclass = {cs.CV},
     author = {Urbano, Alonso and Romero, David W.},
     title = {Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries}
   }