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Organizing time-series data into granular balls can drastically reduce inference time while boosting resilience against mislabeled samples.
AMIE (Video) outperformed human physicians in critical clinical tasks, signaling a leap toward AI that can effectively engage in complex medical consultations.
P-RWKV achieves competitive performance in 3D point cloud representation learning while slashing computational costs and inference latency compared to traditional methods.
Training a foundation model on a trillion minutes of wearable sensor data unlocks surprisingly accurate predictions across a wide range of health conditions, even with limited labeled data.
