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Standard temporal architectures can reliably forecast electrochemical cell degradation across disparate operational campaigns, proving that purely data-driven sequence models can overcome noisy, real-world physical shifts without complex hand-crafted priors.
SLOT-Align achieves superior accuracy and robustness in One-Shot Federated Learning by harmonizing feature representations in the presence of domain and label shifts.
Generative models can now be rigorously applied to compressed sensing in infinite-dimensional spaces, enabling stable signal recovery with resolution-independent sampling.
