Results 31 to 40 of about 28,952 (292)
On the discrepancy principle for stochastic gradient descent
Inverse Problems, 2020 Abstract Stochastic gradient descent (SGD) is a promising numerical method for solving large-scale inverse problems. However, its theoretical properties remain largely underexplored in the lens of classical regularization theory. In this note, we study the classical discrepancy principle, one of the most popular a posteriori choice rules,
Tim Jahn, Bangti Jin
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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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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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Pengembangan Stochastic Gradient Descent Dengan Penambahan Variabel Tetap [PDF]
, 2022 Stochastic Gradient Descent (SGD) adalah salah satu dari optimizer yang sering digunakan dalam deep learning, maka dari itu dalam penelitian ini akan melakukan sebuah modifikasi terhadap Stochastic Gradient Descent (SGD). Stochastic Gradient Descent (SGD)
Adimas Tristan Nagara Hartono
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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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Benign Underfitting of Stochastic Gradient Descent
Advances in Neural Information Processing Systems 35, 2022 We study to what extent may stochastic gradient descent (SGD) be understood as a "conventional" learning rule that achieves generalization performance by obtaining a good fit to training data. We consider the fundamental stochastic convex optimization framework, where (one pass, without-replacement) SGD is classically known to minimize the population ...
Tomer Koren +3 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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Edumatic, 2020 Kopi Arabika merupakan salah satu minuman favorit bagi banyak orang. Dalam pembuatannya, kopi arabika memiliki takaran yang berbeda disetiap negara, yang menghasilkan kualitas yang berbeda pula. Penelitian kopi arabika ini menggunakan dataset yang berisi
Veronica Retno Sari +2 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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DEVELOPMENT OF R PACKAGE AND EXPERIMENTAL ANALYSIS ON PREDICTION OF THE CO2 COMPRESSIBILITY FACTOR USING GRADIENT DESCENT [PDF]
Journal of Engineering Science and Technology, 2018 Nowadays, many variants of gradient descent (i.e., the methods included in machine learning for regression) have been proposed. Moreover, these algorithms have been widely used to deal with real-world problems.
LALA SEPTEM RIZA +5 more
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