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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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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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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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