SGDA generates synthetic faults in the frequency domain from healthy signals to augment training data for ML-based induction motor diagnostics, claiming superior accuracy.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
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
-
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.