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Found 5 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.
Analysis and Comparison of Two-Level KFAC Methods for Training Deep Neural Networks
As a second-order method, the Natural Gradient Descent (NGD) has the ability to accelerate training of neural networks.
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.
Efficient Approximations of the Fisher Matrix in Neural Networks using Kronecker Product Singular Value Decomposition
Several studies have shown the ability of natural gradient descent to minimize the objective function more efficiently than ordinary gradient descent based methods.
Factored couplings in multi-marginal optimal transport via difference of convex programming
Optimal transport (OT) theory underlies many emerging machine learning (ML) methods nowadays solving a wide range of tasks such as generative modeling, transfer learning and information retrieval.
