A gradient-dependence-guided sparse learning framework reduces multi-task interference by combining Fisher information utility estimates with dynamic gradient trajectory tracking.
A new model called MIXTAP uses multi-task learning and knowledge distillation to jointly predict intra- and inter-protein PTM crosstalk, outperforming ...
A new multi-task adversarial autoencoder framework generates functional genomic sequences that preserve biophysical properties and emergent biological structure, outperforming standard ...
A single attention-gated multi-task network achieved classification and segmentation accuracy comparable to separate dedicated models on breast ultrasound images while ...
A dual-decoder deep-learning network localized 14 shoulder bony landmarks on contralateral CT scans with a mean radial error of 2.1 ...
UAC-Net combined contextual attention and multi-scale feature fusion in a shared neural network that classified three brain-tumor types and delineated ...
A multi-task deep learning system reads infant hip ultrasounds, segments seven anatomical structures, localizes six landmarks, and measures Graf angles ...
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