Fixed isotropic marginals in JEPAs can be maximally misaligned with unknown structured geometries, and HamJEPA using symplectic Hamiltonian leapfrog maps improves kNN and linear-probe performance on CIFAR-100 and ImageNet-100.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
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Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction
Fixed isotropic marginals in JEPAs can be maximally misaligned with unknown structured geometries, and HamJEPA using symplectic Hamiltonian leapfrog maps improves kNN and linear-probe performance on CIFAR-100 and ImageNet-100.
- Language Modeling with Hyperspherical Flows