eNMF is a new exterior-point algorithm for NMF that initializes from unconstrained factorization, applies a rotation to reach the nonnegative boundary, and empirically outperforms 81 baseline combinations on real and synthetic data.
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A neurosymbolic model augments Swin Transformers with focal sets and fuzzy logic to produce calibrated hierarchical image classifications that respect logical constraints.
Principal Nested Cones is a nonlinear dimension reduction technique that projects cone-structured data onto nested lower-dimensional cones to jointly represent size and shape variation.
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An Exterior Method for Nonnegative Matrix Factorization
eNMF is a new exterior-point algorithm for NMF that initializes from unconstrained factorization, applies a rotation to reach the nonnegative boundary, and empirically outperforms 81 baseline combinations on real and synthetic data.
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A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification
A neurosymbolic model augments Swin Transformers with focal sets and fuzzy logic to produce calibrated hierarchical image classifications that respect logical constraints.
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Principal Nested Cones
Principal Nested Cones is a nonlinear dimension reduction technique that projects cone-structured data onto nested lower-dimensional cones to jointly represent size and shape variation.