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Theory of learning of high-dimensional controlled non-linear dynamical systems (I): models and methods

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arXiv:2606. 07247v1 Announce Type: cross Abstract: Neural ordinary differential equations (neural ODEs) have rapidly gained prominence as a powerful and unifying framework for conceptualizing artificial neural networks, elegantly connecting the continuous-time modeling of dynamical systems with the discrete, data-driven paradigm of modern deep learning.

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Authors: Pierfrancesco Urbani
Released: 2026-06-08 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Theory of learning of high-dimensional controlled non-linear dynamical systems (I): models and methods overlaps with 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)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, token, line, control, trained, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.07247

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