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PAM-GS is proposed, a physics-aware gradient surgery method that adaptively mitigates task interference during training according to the observed conflict types, and demonstrates that PAM-GS combines competitive solution accuracy with consistently strong task-balanced performance, outperforming existing methods on most...
Operator-theoretic generalization bounds for deep multi-output function classes are developed by representing network layers as Koopman composition operators on vector-valued reproducing kernel Hilbert spaces and derive Rademacher complexity bounds for invertible and width-expanding injective architectures.
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