DAD4TS trains a diffusion-based generator jointly with a forecaster under RL control and geometric projections to produce augmentation samples that boost accuracy on small-scale time-series data, with validation reported on five of six real-world datasets.
Synthetic mobility feature generation for mental health prediction using diffusion models
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A2G-DiffRec applies adaptive autoguidance in diffusion recommenders, learning to balance main and weak model outputs via fairness-aware regularization to improve item exposure fairness with only marginal accuracy loss.
citing papers explorer
No citing papers match the current filters.