A Momentum-incorporated Fast Parallelized Stochastic Gradient Descent for Latent Factor Model in Shared Memory Systems ; Electronic ISBN: 978-1-7281-6855-5.
A new approach for the complete modal decomposition of the optical fields emerging from the multimode fiber is presented in this paper. Based on the stochastic ...
Abstract—Latent factor (LF) model is an effective method for extracting useful knowledge from high- dimensional and sparse (HiDS) data generated by various.
Latent factor (LF) analysis via stochastic gradient descent (SGD) is greatly efficient in discovering latent patterns from them. However, as a sequential ...
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A Momentum-incorporated Fast Parallelized Stochastic Gradient Descent for Latent Factor Model in Shared Memory Systems. ICNSC 2020: 1-6. [c2]. view. electronic ...
A Momentum-incorporated Fast Parallelized Stochastic Gradient Descent for Latent Factor Model in Shared Memory Systems. H Gou, J Li, W Qin, C He, Y Zhong, R Che.
In this article, we develop a fast parallel SG method, FPSG, for shared memory systems. By dramatically reducing the cache-miss rate and carefully addressing ...
This work incorporates a generalized Nesterov's accelerated gradient method into the mentioned learning algorithm, thereby achieving a new algorithm and ...
A recommender system (RS) relying on latent factor analysis usually adopts stochastic gradient descent (SGD) as its learning algorithm. However, owing to its ...
Abstract—Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a.