Results 21 to 30 of about 23,797 (211)
Training a Two-Layer ReLU Network Analytically
Sensors, 2023 Neural networks are usually trained with different variants of gradient descent-based optimization algorithms such as the stochastic gradient descent or the Adam optimizer.
Adrian Barbu
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Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization
Fractal and Fractional, 2022 Stochastic gradient descent is the method of choice for solving large-scale optimization problems in machine learning. However, the question of how to effectively select the step-sizes in stochastic gradient descent methods is challenging, and can ...
Ruijuan Chen, Xiaoquan Tang, Xiuting Li
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Stochastic gradient descent for hybrid quantum-classical optimization [PDF]
Quantum, 2020 Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of parameterized quantum circuits. In this work, we explore the consequences
Ryan Sweke +6 more
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Pipelined Stochastic Gradient Descent with Taylor Expansion
Applied Sciences, 2023 Stochastic gradient descent (SGD) is an optimization method typically used in deep learning to train deep neural network (DNN) models. In recent studies for DNN training, pipeline parallelism, a type of model parallelism, is proposed to accelerate SGD ...
Bongwon Jang, Inchul Yoo, Dongsuk Yook
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Natural Evolutionary Gradient Descent Strategy for Variational Quantum Algorithms
Intelligent Computing, 2023 Recent research has demonstrated that parametric quantum circuits (PQCs) are affected by gradients that progressively vanish to zero as a function of the number of qubits.
Jianshe Xie +4 more
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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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Semi-Stochastic Gradient Descent Methods
Frontiers in Applied Mathematics and Statistics, 2017 In this paper we study the problem of minimizing the average of a large number of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient
Jakub Konečný, Peter Richtárik
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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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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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