Riemannian Cramer: linearities optimizer

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Lecture Notes in Mathematics. 8–25 Paul Malliavin Elliptic estimates and diffusions in Riemannian geometry and complex analysis 26–34 P. Greenwood and. Shanebrook Application of fractional differentiation to the modeling of hodograph linearities . . . . . . . . . 289–293.

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Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case Konstantinos Slavakis, Senior Member, IEEE, Shiva Salsabilian, David S. Wack, Sarah F. Muldoon, Henry E. Baidoo-Williams, Jean M. Vettel, Matthew Cieslak, and Scott T. Grafton Abstract-This paper advocates Riemannian multi-manifold

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function used to train the neural networks is Mean Square Error, the optimizer associated was ADAM [18], and the non-linearities were implemented as ReLU-s. Training times for the networks were roughly 15 20 mins. Once trained, the network was tested on a gamut of test datasets described in section 3.

Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer’s disease arXiv preprint arXiv:1711.08359 November 22, 2017

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For the case when M is a Riemannian subman-ifold of R m , formed by a set of equality constraints, we show that the optimality conditions can be derived directly from the traditional results on R m .