New Hopf algebras help classify 4D shapes.
problem Classifying 4D shapes up to deformations.
method Developed non-factorizable ribbon Hopf algebras.
result Some derived invariants are boundary-dependent.
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3 results for “non-factorizable”
SIGMA prior enables federated learning for non-factorizable models.
problem Current FL methods assume conditional independence, limiting applicability to non-factorizable models.
method SIGMA prior approximates deep generative model to induce conditional independence structure.
result SIGMA prior expands FL applicability to fields requiring modeling dependencies.
VI struggles to fully quantify uncertainty when distributions don't factorize.
problem Uncertainty quantification in non-factorizable distributions.
method Analysis of variational inference trade-offs and divergence choices.
result Different divergences yield different measures of uncertainty in VI.