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Dec 22, 2023 · In this paper, we fill this theoretical gap by establishing a non-asymptotic convergence bound for stochastic heavy-ball methods with step decay ...
Heavy-ball momentum with decaying learning rates is widely used with SGD for optimizing deep learning models. In contrast to its empirical popularity, the.
Mar 17, 2024 · Heavy-ball momentum with decaying learning rates is widely used with SGD for optimizing deep learning models. In contrast to its empirical ...
Facts about Stochastic Heavy Ball (SHB) Method: In practice, SHB is widely adopted to provide acceleration. In theory, few results show SHB can provide ...
Dive into the research topics of 'ACCELERATED CONVERGENCE OF STOCHASTIC HEAVY BALL METHOD UNDER ANISOTROPIC GRADIENT NOISE'. Together they form a unique ...
Stochastic Heavy Ball Method accelerates convergence with step decay scheduler on quadratic objectives under anisotropic gradient noise.
In this paper, we fill this theoretical gap by establishing a non-asymptotic convergence bound for stochastic heavy-ball methods with step decay scheduler on ...
On the Convergence of Stochastic Gradient Descent with ... Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
ACCELERATED CONVERGENCE OF STOCHASTIC HEAVY BALL METHOD UNDER ANISOTROPIC GRADIENT NOISE. Paper presented at 12th International Conference on Learning ...
It is well-known that stochastic gradient noise (SGN) in stochastic optimization acts as implicit regularization for deep learning and is essentially important ...