SGDA generates synthetic faults in the frequency domain from healthy signals to augment training data for ML-based induction motor diagnostics, claiming superior accuracy.
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Proposes a modular architecture for LLM-based wellbeing recommenders using explicit constraints on guidance, explanations, directness, and user control to address trust calibration, intent alignment, and consequence awareness.
Introduces semantic Pareto-DQN for multi-objective recommendation that sustains trajectory variance to improve diversity and fairness on MovieLens with limited engagement loss.
citing papers explorer
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Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics
SGDA generates synthetic faults in the frequency domain from healthy signals to augment training data for ML-based induction motor diagnostics, claiming superior accuracy.
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Designing Trustworthy LLM-based Wellbeing Recommendation through Controllable Interaction
Proposes a modular architecture for LLM-based wellbeing recommenders using explicit constraints on guidance, explanations, directness, and user control to address trust calibration, intent alignment, and consequence awareness.
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Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
Introduces semantic Pareto-DQN for multi-objective recommendation that sustains trajectory variance to improve diversity and fairness on MovieLens with limited engagement loss.