Results 51 to 60 of about 1,338,397 (280)
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
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 +4 more sources
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
openaire +2 more sources
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
doaj +1 more source
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
openaire +2 more sources
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
doaj +1 more source
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
Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing
Jurnal Teknologi Informasi dan Ilmu Komputer, 2018 Klasifikasi merupakan teknik dalam data mining untuk mengelompokkan data berdasarkan keterikatan data terhadap data sampel. Pada penelitian ini, kami melakukan perbandingan 9 teknik klasifikasi untuk mengklasifikasi respon pelanggan pada dataset Bank ...
Irvi Oktanisa, Ahmad Afif Supianto
doaj +1 more source
Counterexamples for Noise Models of Stochastic Gradients
Examples and Counterexamples, 2023 Stochastic Gradient Descent (SGD) is a widely used, foundational algorithm in data science and machine learning. As a result, analyses of SGD abound making use of a variety of assumptions, especially on the noise behavior of the stochastic gradients ...
Vivak Patel
doaj +1 more source