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Van Wallendael

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Articles by Van Wallendael

Computer Science > Computer Vision and Pattern Recognition

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By Van Wallendael
| arXiv
Novel view synthesis (NVS) models can produce realistic new views of the same scene from different viewpoints. However, these generated views are not always geometrically consistent with one another. Multi-view (MV) consistency has shown promise as a tool for evaluating these NVS models.
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Beyond binary AKI classification: development and external validation of a distributional model predicting serum creatinine and urine output trajectories in ICU patients

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By Van Biesen, de Waele, Van Wallendael
| link.springer.com
Study data sources & population We used four large-scale ICU datasets spanning diverse healthcare systems and geographic regions. The training cohort consisted of adult ICU patients from the MIMIC-IV (v3.1) database [15], a publicly available critical care resource containing de-identified records from a single center in Boston, USA.
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POTR: Post-Training 3DGS Compression

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By Van Wallendael
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Generative Anchored Fields: Controlled Data Generation via Emergent Velocity Fields and Transport Algebra

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By Van Wallendael, Deressa Wodajo
| arXiv
arXiv:2511.22693 (cs) View PDF HTML (experimental) Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2511.22693 [cs.LG] (or arXiv:2511.22693v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2511.22693 Submission history From: Deressa Wodajo [ view email] [v1] Thu, 27 Nov 2025 18:48:42 UTC (18,135 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What...
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[2503.14106] Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction

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By Van Wallendael, Van Hoecke
| arXiv
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landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images

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By Van Wallendael, Van Hoecke
| arXiv
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TGIF: Text-Guided Inpainting Forgery Dataset

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By Van Wallendael
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2404.15018] Conformal Predictive Systems Under Covariate Shift

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By Van Wallendael, Van Hoecke
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2402.09062] Blind Deep-Learning-Based Image Watermarking Robust Against Geometric Transformations

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By Van Wallendael
| arXiv
arXiv:2402.09062 (cs) Download PDF Comments: Subjects: Multimedia (cs.MM); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2402.09062 [cs.MM] (or arXiv:2402.09062v1 [cs.MM] for this version) https://doi.org/10.48550/arXiv.2402.09062 Submission history From: Hannes Mareen PhD [ view email] [v1] Wed, 14 Feb 2024 10:18:00 UTC (417 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is...
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[2402.04906] Conformal Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects

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By Van Wallendael, Van Hoecke
| arXiv
arXiv:2402.04906 (cs) Download PDF Comments: Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2402.04906 [cs.LG] (or arXiv:2402.04906v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2402.04906 Submission history From: Jef Jonkers [ view email] [v1] Wed, 7 Feb 2024 14:35:25 UTC (291 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are...
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