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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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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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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
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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
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Efficiency Ordering of Stochastic Gradient Descent
Advances in Neural Information Processing Systems 35, 2022 To appear in NeurIPS ...
Jie Hu 0027 +2 more
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Asynchronous Decentralized Accelerated Stochastic Gradient Descent [PDF]
IEEE Journal on Selected Areas in Information Theory, 2021 In this work, we introduce an asynchronous decentralized accelerated stochastic gradient descent type of method for decentralized stochastic optimization, considering communication and synchronization are the major bottlenecks. We establish $\mathcal{O}(1/ε)$ (resp., $\mathcal{O}(1/\sqrtε)$) communication complexity and $\mathcal{O}(1/ε^2)$ (resp ...
Guanghui Lan, Yi Zhou 0015
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Damped Newton Stochastic Gradient Descent Method for Neural Networks Training
Mathematics, 2021 First-order methods such as stochastic gradient descent (SGD) have recently become popular optimization methods to train deep neural networks (DNNs) for good generalization; however, they need a long training time.
Jingcheng Zhou +3 more
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Stochastic Gradient Descent with Polyak’s 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
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Normalized stochastic gradient descent learning of general complex‐valued models
Electronics Letters, 2021 The stochastic gradient descent (SGD) method is one of the most prominent first‐order iterative optimisation algorithms, enabling linear adaptive filters as well as general nonlinear learning schemes.
T. Paireder, C. Motz, M. Huemer
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One-step corrected projected stochastic gradient descent for statistical estimation [PDF]
A generic, fast and asymptotically efficient method for parametric estimation is described. It is based on the projected stochastic gradient descent on the log-likelihood function corrected by a single step of the Fisher scoring algorithm.
Esstafa, Youssef, Brouste, Alexandre
core +4 more sources