In this work, we introduce a perturbation technique for sampling into optimization for strongly convex functions. We show that perturbation applied to the ...
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First, we present new continuous dynamics using skew-symmetric matrices that converge more rapidly than the gradient flow under mild conditions. To show faster ...
An optimization algorithm for strongly convex functions with a novel discretization framework that combines the Euler method with the leapfrog method which ...
Is the Performance of My Deep Network Too Good to Be True? · Skew-symmetrically perturbed gradient flow for convex optimization · Mediated Uncoupled Learning: ...
Skew-symmetrically perturbed gradient flow for convex optimization. F Futami, T Iwata, N Ueda, I Yamane. Asian Conference on Machine Learning, 721-736, 2021. 1 ...
Skew-symmetrically perturbed gradient flow for convex optimization. F Futami, T Iwata, N Ueda, I Yamane. Asian Conference on Machine Learning, 721-736, 2021. 1 ...
Skew-symmetrically perturbed gradient flow for convex optimization. F. Futami. Scalable gradient matching based on state space Gaussian Processes (Selected ...
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Jul 15, 2024 · Skew-symmetrically perturbed gradient flow for convex optimization. ACML 2021: 721-736. [c6]. view. electronic edition @ mlr.press (open access) ...
Skew-symmetrically perturbed gradient flow for convex optimization. In Proceedings of the 13th Asian Conference on Machine Learning (ACML 2021), Proceedings ...
Skew-symmetrically perturbed gradient flow for convex optimization · pdf icon ... Regularized Multitask Learning for Multidimensional Log-Density Gradient ...