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Confidence miscalibration in medical AI can be mitigated, leading to both higher diagnostic accuracy and improved trust in clinical applications.
DAVE achieves superior speech separation performance even in the presence of unreliable visual inputs, thanks to its innovative decoupled framework and extensive training dataset.
Ten teams achieved groundbreaking advancements in low-light image enhancement, with improvements that redefine the state-of-the-art in burst photography.
Extreme heat is not just a risk factor; it amplifies fault predictions over time, impacting nearly 30% of EV charging posts and reshaping maintenance strategies for climate resilience.
PFAdapter cuts communication costs by nearly 50% while boosting accuracy in personalized federated learning for MLLMs.
Achieving top-tier performance in speaker extraction from real conversations with a novel training framework that leverages proxy supervision and a large-scale dataset.
