arXiv Machine Learning By Minhui Lu, Joshua D. Reiss

Band-Count Dense Modal Estimation with Fixed-Frequency Differentiable Resonator Refinement

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arXiv:2608. 00667v1 Announce Type: cross Abstract: Task B of the 1st DAFx Parameter Estimation Challenge requires estimating the frequencies, decay rates, gains, and number of modes in a dense plate-reverb impulse response.

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arXiv AI
Aug 25

FreKoo++: Learning Continuous Spectral Dynamics for Temporal Domain Generalization

FreKoo++ is a continuous spectral-dynamical framework designed for Temporal Domain Generalization (TDG). It unifies continuous Koopman modal dynamics with adaptive spectral disentanglement, mapping source-domain parameters into a latent space and modeling their evolution as a superposition of learnable continuous modes. The method handles irregular timestamps, supports arbitrary horizon extrapolation, and introduces an adaptive soft spectral weighting mechanism that isolates persistent dynamics from transient noise, achieving state‑of‑the‑art performance on discrete and continuous TDG benchmarks.

By En Yu, Xiaoyu Yang, Wei Duan, Guangquan Zhang, Jie Lu
arXiv Machine Learning
Jun 3

Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations

arXiv:2606. 03936v1 Announce Type: new Abstract: Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequency content is systematically attenuated, limiting reliability where fine-scale structure matters.

By Niccol\`o Perrone, Fanny Lehmann, Stefania Fresca, Filippo Gatti