This paper tackles the Fourier Transform (FT) PR problem for sparse signals. We recast the FT PR as a novel optimization problem on the Riemannian manifold by leveraging the sparsity and structural ...

This limitation hinders the representation learning capability of the models. To address this issue, this paper focuses on how to better extend the representation learning from a single space to ...

Another innovative technique is Discrete Metric Learning (DML), which leverages the Riemannian manifold to facilitate fast image set classification. By minimizing the Hamming distance between ...

Model-based Distributionally Robust Optimisation: Bayesian Ambiguity Sets and Model Misspecification ...

Nature Research Intelligence Topics enable transformational understanding and discovery in research by categorising any document into meaningful, accessible topics. Read this blog to understand ...

They appear in a very natural manner in many areas of mathematics and physics. On a differential manifold or more generally on a geodesic metric space, one investigates geometric and analytic ...

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