[2602.12267] Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data

Computer Science > Machine Learning arXiv:2602.12267 (cs) [Submitted on 12 Feb 2026 (v1), last revised 2 Mar 2026 (this version, v2)] Title:Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data Authors:Duy Nguyen, Jiachen Yao, Jiayun Wang, Julius Berner, Animashree Anandkumar View a PDF of the paper titled Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data, by Duy Nguyen and 4 other authors View PDF HTML (experimental) Abstract:Self-supervised learning (SSL) is a powerful paradigm for learning from unlabeled time-series data. However, popular methods such as masked autoencoders (MAEs) rely on reconstructing inputs from a fixed, predetermined masking ratio. Instead of this static design, we propose treating the corruption level as a new degree of freedom for representation learning, enhancing flexibility and performance. To achieve this, we introduce the Flow-Guided Neural Operator (FGNO), a novel framework combining operator learning with flow matching for SSL training. FGNO learns mappings in functional spaces by using Short-Time Fourier Transform to unify different time resolutions. We extract a rich hierarchy of features by tapping into different network layers and flow times that apply varying strengths of noise to the input data. This enables the extraction of versatile representations, from low-level patterns to high-level global features, using a single model adaptable to specific tasks. Unlike prior generative SSL me...