0000-0002-0231-9950

Up a level
Export as [feed] RSS
Group by: Date | Item Type | Refereed | No Grouping
Jump to: Yes

Yes

Doorenbos, Lars; Sznitman, Raphael; Márquez-Neila, Pablo (October 2024). Learning non-linear invariants for unsupervised out-of-distribution detection. In: European Conference on Computer Vision.

Moghani, Masoud; Doorenbos, Lars; Panitch, William Chung-Ho; Huver, Sean; Azizian, Mahdi; Goldberg, Ken; Garg, Animesh (October 2024). SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants. In: International Conference on Intelligent Robots and Systems.

Doorenbos, Lars; Márquez Neila, Pablo; Sznitman, Raphael; Mettes, Pascal (2024). Hyperbolic Random Forests. Transactions on machine learning research, 2024(05) OpenReview.net

Jungo, Alain; Doorenbos, Lars; Da Col, Tommaso; Beelen, Maarten; Zinkernagel, Martin; Márquez-Neila, Pablo; Sznitman, Raphael (2023). Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery. International journal of computer assisted radiology and surgery, 18(6), pp. 1085-1091. Springer 10.1007/s11548-023-02909-y

Zbinden, Lukas; Doorenbos, Lars; Pissas, Theodoros; Huber, Adrian Thomas; Sznitman, Raphael; Márquez Neila, Pablo (2023). Stochastic Segmentation with Conditional Categorical Diffusion Models. In: International Conference on Computer Vision (ICCV) 2023. arxiv. Cornell University 10.48550/arXiv.2303.08888

Doorenbos, Lars; Cavuoti, Stefano; Longo, Giuseppe; Brescia, Massimo; Sznitman, Raphael; Márquez-Neila, Pablo (3 December 2022). Generating astronomical spectra from photometry with conditional diffusion models (In Press). In: Machine Learning and the Physical Sciences.

Doorenbos, Lars Jelte; Torbaniuk, Olena; Cavuoti, Stefano; Paolillo, Maurizio; Longo, Giuseppe; Brescia, Massimo; Sznitman, Raphael; Márquez-Neila, Pablo (2022). ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection. Astronomy and astrophysics, 666, A171. EDP Sciences 10.1051/0004-6361/202243900

Provide Feedback