Hang Yu

C2GA transforms respiratory sound classification by generating high-fidelity, class-consistent audio augmentations that enhance model performance in real-world clinical scenarios.

Shifting from explicit domain descriptions to an inductive approach, DOMINO generates high-quality synthetic data that enhances LLM performance without the need for manual prompt engineering.

Developer-style keyword searches completely nullify the advantage of even the best code embedding models, highlighting a critical gap in current code search techniques.

Forget scaling model size – QuitoBench reveals that simply scaling training data delivers bigger gains for time series forecasting, across both deep learning and foundation models.