Jul 15, 2022 · Abstract:This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative ...

This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses.

This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses.

Mar 16, 2023 · We study the natural gradient methods for the large-scale decentralized optimization problems on Riemannian manifolds, where the local objective ...

Next, we shall explain how the natural gradient descent is related to the mirror descent and the ordinary gradient when the Riemannian space Θ is dually flat.

Natural Gradient Descent is an approximate second-order optimisation method. It has an interpretation as optimizing over a Riemannian manifold using an ...

Jan 7, 2023 · Natural-gradient descent can be derived from a first-order (linear) approximation of the geodesic, which implies that natural-gradient descent ...

Abstract. This paper studies large-scale optimization problems on Riemannian manifolds whose. 4 objective function is a finite sum of negative ...

Feb 22, 2019 · It generalizes the optimization methods from Euclidean spaces onto Riemannian manifolds. Specifically, in the gradient descent method, adapting ...

Feb 16, 2018 · In this post, we discuss the natural gradient, which is the direction of steepest descent in a Riemannian manifold [1], and present the main ...