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Achieve ~20% gains on difficult 3D medical image segmentation by explicitly removing noisy activations in U-Net skip connections with a novel proximal-sparse attention mechanism.
Lipschitz-constrained Transformers, built from gradient flows, can provably approximate any Lipschitz-continuous function, offering a path to more robust and stable architectures.
