Results 51 to 60 of about 28,952 (292)
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
Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework
Tongxin xuebao, 2018 Aiming at the contradiction between the efficiency and privacy of stochastic gradient descent algorithm in distributed computing environment,a stochastic gradient descent algorithm preserving differential privacy based on MapReduce was proposed.Based on ...
Yihan YU, Yu FU, Xiaoping WU
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Equating quantum imaginary time evolution, Riemannian gradient flows, and stochastic implementations
Physical Review ResearchWe identify quantum imaginary time evolution as a Riemannian gradient flow on the unitary group. We develop an upper bound for the error between the two evolutions that can be controlled through the step size of the Riemannian gradient descent that ...
Nathan A. McMahon +2 more
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Graph Drawing by Stochastic Gradient Descent [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2019 Submitted to IEEE Transactions on Visualization and Computer Graphics on 26/06 ...
Jonathan X. Zheng +2 more
openaire +4 more sources