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Grounding audio understanding in structured auditory scenes with a hybrid perception-reasoning framework dramatically improves performance, rivaling that of much larger models.
Label inference attacks in vertical federated learning don't work because bottom models are good at representing labels, but because of feature-label distribution alignment, opening the door to simple, effective defenses.
Medical VQA models can be made significantly more robust to adversarial attacks using a novel pre-training approach based on masked autoencoders and variational inference, without requiring additional data or complex procedures.
