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Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction

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arXiv:2606. 06351v1 Announce Type: new Abstract: Vessel trajectory prediction from Automatic Identification System (AIS) data is essential for maritime situational awareness, yet it remains challenging due to irregular sampling, missing reports, and complex dynamics.

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Authors: Jaeyeong Lee, Wonmo Koo, Heeyoung Kim
Released: 2026-06-05 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, rect, eval, line, rate, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.06351

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