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publications
FreIE Low-Frequency Spectral Bias in Neural Networks for Time-Series Tasks
Published in IEEE International Conference on Data Mining (ICDM), 2025
We demonstrate that spectral bias—the tendency of neural networks to fit low-frequency signals first—is a universal trait in time-series prediction, not an architectural artifact. To mitigate this, we propose FreLE, an algorithm using frequency regularization to improve model generalization. Extensive experiments confirm FreLE’s effectiveness.
talks
PAC Learning Framework
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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