Derives deterministic MMD, KSD, and KL objectives with rotationally invariant kernels on the hypersphere, yielding more stable SSL training and dataset-dependent geometry in learned representations.
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Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere
Derives deterministic MMD, KSD, and KL objectives with rotationally invariant kernels on the hypersphere, yielding more stable SSL training and dataset-dependent geometry in learned representations.