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Creators/Authors contains: "Whiting, Wes"

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  1. ; ; (, Communications on Applied Mathematics and Computation)
    Abstract We prove, under mild conditions, the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model, both in batch gradient descent and stochastic gradient descent. We also discuss a Riemannian version of the Adam algorithm. We show numerical simulations of these algorithms on various benchmarks. 
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