Výsledky vyhľadávania - "stochastic/gradient-based optimization algorithm"

Optimally Designed Shell Enclosures with Tuned Absorbers for Minimizing Sound Power Autor Constans, Eric W., Belegundu, Ashok D., Koopmann, Gary H.

“… The design approach combines a finite element method vibration prediction code, a boundary element method sound power prediction code and a combined stochastic/gradient-based optimization algorithm…”
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Improving Deep Neural Networks' Training for Image Classification With Nonlinear Conjugate Gradient-Style Adaptive Momentum Autor Wang, Bao, Ye, Qiang

“…Momentum is crucial in stochastic gradient-based optimization algorithms for accelerating or improving training deep neural networks (DNNs…”
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Quasi-Newton optimised Kolmogorov-Arnold Networks for wind farm power prediction Autor Mubarak, Auwalu Saleh, Ameen, Zubaida Said, Mati, Sagiru, Lasisi, Ayodele, Naveed, Quadri Noorulhasan, Abdulkadir, Rabiu Aliyu

“…Having accurate and effective wind energy forecasting that can be easily incorporated into smart networks is important. Appropriate planning and energy…”
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Improving Deep Neural Networks’ Training for Image Classification With Nonlinear Conjugate Gradient-Style Adaptive Momentum Autor Wang, Bao, Ye, Qiang

“…Momentum is crucial in stochastic gradient-based optimization algorithms for accelerating or improving training deep neural networks (DNNs…”
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Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization Autor Sun, Tao, Ling, Huaming, Shi, Zuoqiang, Li, Dongsheng, Wang, Bao

“…Heavy ball momentum is crucial in accelerating (stochastic) gradient-based optimization algorithms for machine learning…”
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Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum Autor Wang, Bao, Ye, Qiang

“…Momentum plays a crucial role in stochastic gradient-based optimization algorithms for accelerating or improving training deep neural networks (DNNs…”
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Bounding the expected run-time of nonconvex optimization with early stopping Autor Flynn, Thomas, Yu, Kwang Min, Malik, Abid, D'Imperio, Nicolas, Yoo, Shinjae

“…This work examines the convergence of stochastic gradient-based optimization algorithms that use early stopping based on a validation function…”
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Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis Autor Wang, Weiran, Wang, Jialei, Garber, Dan, Srebro, Nathan

“… Although several stochastic gradient based optimization algorithms have been recently proposed to solve this problem, no global convergence guarantee was provided by any of…”
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