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Dynamic Transfer Learning across Graphs
Transferring knowledge across graphs plays a pivotal role in many high-stake domains, ranging from transportation networks to e-commerce networks, from neuroscience to finance.
Geometry-Aware Hierarchical Bayesian Learning on Manifolds
Furthermore, we incorporate the feature learning of neural networks with the feature aggregation of Bayesian models to investigate the feasibility of jointly learning on manifolds.
An Optimal Transportation Based Univariate Neuroimaging Index
We compute the OT from each image to a template and measure the Wasserstein distance between them.
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