Results 31 to 40 of about 22,674 (253)
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
openaire +3 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj
Design of Momentum Fractional Stochastic Gradient Descent for Recommender Systems
IEEE Access, 2019 The demand for recommender systems in E-commerce industry has increased tremendously. Efficient recommender systems are being proposed by different E-business companies with the intention to give users accurate and most relevant recommendation of ...
Zeshan Aslam Khan +4 more
doaj +1 more source
Efficiency Ordering of Stochastic Gradient Descent
Advances in Neural Information Processing Systems 35, 2022 To appear in NeurIPS ...
Jie Hu 0027 +2 more
openaire +3 more sources
Stochastic Gradient Descent with Polyakâs Learning Rate [PDF]
Journal of Scientific Computing, 2021 Stochastic gradient descent (SGD) for strongly convex functions converges at the rate $\bO(1/k)$. However, achieving good results in practice requires tuning the parameters (for example the learning rate) of the algorithm. In this paper we propose a generalization of the Polyak step size, used for subgradient methods, to Stochastic gradient descent. We
Mariana Oliveira Prazeres +1 more
openaire +2 more sources
Correspondence between neuroevolution and gradient descent
Nature Communications, 2021 Gradient-based and non-gradient-based methods for training neural networks are usually considered to be fundamentally different. The authors derive, and illustrate numerically, an analytic equivalence between the dynamics of neural network training under
Stephen Whitelam +3 more
doaj +1 more source
Jurnal Elkomika, 2022 ABSTRAK Terdapat banyak variable nonlinear dalam sistem kendali untuk quadcopter sehingga cukup rumit untuk mengatur dinamika penerbangan wahana ini. Untuk mengatasi masalah tersebut akan dikembangkan suatu skema sistem kendali Direct Inverse Control ...
MUHAMMAD SABILA HAQQI +1 more
doaj +1 more source