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Riemannian Cramer: linearities optimizer

Riemann manifolds from Hellinger distance.. can be seen as the metric of a Riemannian manifold, Fisher-Rao metric.. approach in approximation of PDEs with 1. new types of non-linearities.

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The sub-Riemannian analogues of Euclidean space are a class of Lie groups endowed with left-invariant sub-Riemannian metrics, christened Carnot groups by the Gromov school, homogeneous groups by Stein and his school, and their Lie algebras named symbol algebras by Tanaka’s school.

the parameters, identifying the underlying Riemannian manifold on which the optimization or sampling are taking place. Samples or optimization steps are conse-quently proposed based on the geometry of the manifold. We apply our framework to stochastic gradient descent and stochastic gradient Langevin dynamics and show

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Impact of Markov Random Field optimizer on MRI-based tissue segmentation in the aging brain.. effects of the diastolic pressure decay and vessel wall non-linearities.

We present a survey of recent advances on Big Learning with Bayesian methods, including Bayesian non-parametrics, RegBayes, and scalable inference algorithms and the systems based-on stochastic subsampling or distributed computing. It is helpful to note that our review is not exhaustive.

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Anthony Tri Tran, C & Ha, Q 2018, A quadratic constraint approach to model predictive control of interconnected systems, Springer, Germany. View/Download from: UTS OPUS View description>> Argha, A, Su, S, Li, L, Nguyen, HT & Celler, BG 2018, Advances in Discrete-Time Sliding Mode Control Theory and Applications, CRC Press.

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Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting.

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