Results 41 to 50 of about 142,362 (315)
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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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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Randomized Stochastic Gradient Descent Ascent
, 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 ...
Sebbouh, Othmane +2 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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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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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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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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Recent Advances in Stochastic Gradient Descent in Deep Learning
Mathematics, 2023 In the age of artificial intelligence, the best approach to handling huge amounts of data is a tremendously motivating and hard problem. Among machine learning models, stochastic gradient descent (SGD) is not only simple but also very effective.
Yingjie Tian, Yuqi Zhang, Haibin Zhang
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Accelerated Backpressure Algorithm
, 2013 We develop an Accelerated Back Pressure (ABP) algorithm using Accelerated Dual Descent (ADD), a distributed approximate Newton-like algorithm that only uses local information.
Jadbabaie, Ali +2 more
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