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This paper introduces Flow-Guided Neural Operator (FGNO), a self-supervised learning framework for time-series data that leverages flow matching to dynamically adjust the corruption level during training. FGNO uses Short-Time Fourier Transform to handle varying time resolutions and extracts hierarchical features by applying different levels of noise through network layers and flow times. By training with noisy inputs but extracting representations from clean inputs, FGNO achieves state-of-the-art performance across multiple biomedical time-series tasks, demonstrating robustness to data scarcity and improved representation learning.
Forget fixed masking ratios: this new self-supervised learning approach for time-series data dynamically adjusts noise levels to extract richer, more versatile representations.
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 methods that use noisy inputs during inference, we propose using clean inputs for representation extraction while learning representations with noise; this eliminates randomness and boosts accuracy. We evaluate FGNO across three biomedical domains, where it consistently outperforms established baselines. Our method yields up to 35% AUROC gains in neural signal decoding (BrainTreeBank), 16% RMSE reductions in skin temperature prediction (DREAMT), and over 20% improvement in accuracy and macro-F1 on SleepEDF under low-data regimes. These results highlight FGNO's robustness to data scarcity and its superior capacity to learn expressive representations for diverse time series.
NVIDIA