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Stochastic Gradient Descent in Continuous Time [PDF]
SSRN Electronic Journal, 2017 Stochastic gradient descent in continuous time (SGDCT) provides a computationally efficient method for the statistical learning of continuous-time models, which are widely used in science, engineering, and finance. The SGDCT algorithm follows a (noisy) descent direction along a continuous stream of data.
Sirignano, Justin +1 more
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Preconditioned Stochastic Gradient Descent [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 13 pages, 9 figures. To appear in IEEE Transactions on Neural Networks and Learning Systems.
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Featured Hybrid Recommendation System Using Stochastic Gradient Descent
International Journal of Networked and Distributed Computing (IJNDC), 2021 Beside cold-start and sparsity, developing incremental algorithms emerge as interesting research to recommendation system in real-data environment. While hybrid system research is insufficient due to the complexity in combining various source of each ...
Si Thin Nguyen +3 more
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Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC [PDF]
, 2015 Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference.
Adams R. +20 more
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Stochastic Reweighted Gradient Descent
, 2021 Despite the strong theoretical guarantees that variance-reduced finite-sum optimization algorithms enjoy, their applicability remains limited to cases where the memory overhead they introduce (SAG/SAGA), or the periodic full gradient computation they require (SVRG/SARAH) are manageable.
Hanchi, Ayoub El, Stephens, David A.
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Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup
Photonics, 2021 For a high-power slab solid-state laser, obtaining high output power and high output beam quality are the most important indicators. Adaptive optics systems can significantly improve beam qualities by compensating for the phase distortions of the laser ...
Shiqing Ma +8 more
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Improving Convergence in Therapy Scheduling Optimization: A Simulation Study
Mathematics, 2020 The infusion times and drug quantities are two primary variables to optimize when designing a therapeutic schedule. In this work, we test and analyze several extensions to the gradient descent equations in an optimal control algorithm conceived for ...
Juan C. Chimal-Eguia +2 more
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Granular Elastic Network Regression with Stochastic Gradient Descent
Mathematics, 2022 Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine.
Linjie He +3 more
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The Improved Stochastic Fractional Order Gradient Descent Algorithm
Fractal and Fractional, 2023 This paper mainly proposes some improved stochastic gradient descent (SGD) algorithms with a fractional order gradient for the online optimization problem.
Yang Yang, Lipo Mo, Yusen Hu, Fei Long
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Scaling transition from momentum stochastic gradient descent to plain stochastic gradient descent
, 2021 The plain stochastic gradient descent and momentum stochastic gradient descent have extremely wide applications in deep learning due to their simple settings and low computational complexity. The momentum stochastic gradient descent uses the accumulated gradient as the updated direction of the current parameters, which has a faster training speed ...
Zeng, Kun +3 more
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