Results 41 to 50 of about 22,674 (253)
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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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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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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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
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A Fixed-Point of View on Gradient Methods for Big Data
Frontiers in Applied Mathematics and Statistics, 2017 Interpreting gradient methods as fixed-point iterations, we provide a detailed analysis of those methods for minimizing convex objective functions. Due to their conceptual and algorithmic simplicity, gradient methods are widely used in machine learning ...
Alexander Jung
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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
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Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm
Sensors, 2023 Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics.
Bo Chen +4 more
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An efficient algorithm for data parallelism based on stochastic optimization
Alexandria Engineering Journal, 2022 Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples.
Khalid Abdulaziz Alnowibet +3 more
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Distributed stochastic gradient descent for link prediction in signed social networks
EURASIP Journal on Advances in Signal Processing, 2019 This paper considers the link prediction problem defined over a signed social network, where the relationship between any two network users can be either positive (friends) or negative (foes).
Han Zhang, Gang Wu, Qing Ling
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Molecular Oncology, EarlyView.Breast cancer remains a major cause of cancer death in women, frequently developing endocrine therapy resistance. This study demonstrates that upregulated p21‐activated kinase 1 (PAK1) activity drives resistance to tamoxifen and long‐term estrogen deprivation in ER+ breast cancer models.
Luisa Schwarzmüller +10 more
wiley +1 more source