YOTOnet achieves improved zero-shot cross-domain fault diagnosis on bearing datasets by combining a physics-aware invariant feature distiller with domain-conditioned sparse experts, showing performance scaling as more training domains are added.
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DUET is a global-to-local method that optimizes LLM training data mixtures via Bayesian optimization guided by influence-based selection and feedback from unseen evaluation tasks, with a regret bound showing convergence to the optimal mixture.
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YOTOnet: Zero-Shot Cross-Domain Fault Diagnosis via Domain-Conditioned Mixture of Experts
YOTOnet achieves improved zero-shot cross-domain fault diagnosis on bearing datasets by combining a physics-aware invariant feature distiller with domain-conditioned sparse experts, showing performance scaling as more training domains are added.
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DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
DUET is a global-to-local method that optimizes LLM training data mixtures via Bayesian optimization guided by influence-based selection and feedback from unseen evaluation tasks, with a regret bound showing convergence to the optimal mixture.