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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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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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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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Randomized Stochastic Gradient Descent Ascent
CoRR, 2021 An increasing number of machine learning problems, such as robust or adversarial variants of existing algorithms, require minimizing a loss function that is itself defined as a maximum. Carrying a loop of stochastic gradient ascent (SGA) steps on the (inner) maximization problem, followed by an SGD step on the (outer) minimization, is known as Epoch ...
Othmane Sebbouh +2 more
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jmrmcode/Fitting-functions-by-Gradient-Descent: v1
, 2023 Fitting a function to data using gradient ...
Juan Miguel Requena Mullor
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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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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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