Introduces Wasserstein-on-Wasserstein flow matching that realizes metameasure flows via nested Wasserstein geometry and scalable sliced/linear approximations for generative modeling of transport plans.
IEEE Transactions on Pattern Analysis and Machine Intelligence16(5), 550–554 (1994)
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
Shape- and peak-sensitive goodness functions for Forward-Forward deliver up to 72pp gains over sum-of-squares, reaching 98.2% on MNIST and 89% on Fashion-MNIST.
Introduces formal verification to compute certified neuron range bounds for CKKS-encrypted neural networks, eliminating overflow failures that previously reached 47%.
IDCL adds density-based curriculum learning and density-core guidance to deep image clustering, claiming superior robustness, faster convergence, and flexibility on benchmark datasets.
citing papers explorer
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Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
Introduces Wasserstein-on-Wasserstein flow matching that realizes metameasure flows via nested Wasserstein geometry and scalable sliced/linear approximations for generative modeling of transport plans.
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Selectivity and Shape in the Design of Forward-Forward Goodness Functions
Shape- and peak-sensitive goodness functions for Forward-Forward deliver up to 72pp gains over sum-of-squares, reaching 98.2% on MNIST and 89% on Fashion-MNIST.
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Encrypted Neural Networks without Overflows
Introduces formal verification to compute certified neuron range bounds for CKKS-encrypted neural networks, eliminating overflow failures that previously reached 47%.
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Deep Image Clustering Based on Curriculum Learning and Density Information
IDCL adds density-based curriculum learning and density-core guidance to deep image clustering, claiming superior robustness, faster convergence, and flexibility on benchmark datasets.