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Found 2 papers, 1 papers with code
Revisiting invariances and introducing priors in Gromov-Wasserstein distances
Gromov-Wasserstein distance has found many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformations.
Unbalanced CO-Optimal Transport
With this result in hand, we provide empirical evidence of this robustness for the challenging tasks of heterogeneous domain adaptation with and without varying proportions of classes and simultaneous alignment of samples and features across single-cell measurements.
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