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Keyword

deep learning optimization

Description: Optimization methods develop or apply algorithms for training deep neural networks and improving their convergence or generalization.
OpenAlex ID: keywords/deep-learning-optimization
Deep Learning for Medical Image Processing: Overview, Challenges and the Future
2017 · Muhammad Imran Razzak, Saeeda Naz, et al. · Lecture notes in computational vision and biomechanics · 1,305
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
2016 · Keskar, Nitish Shirish, Mudigere, Dheevatsa, et al. · arXiv (Cornell University) · 1,128
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On optimization methods for deep learning
2011 · Jiquan Ngiam, Adam Coates, et al. · International Conference on Machine Learning · 868
A Survey on Edge Computing Systems and Tools
2019 · Fang Liu, Guoming Tang, et al. · Proceedings of the IEEE · 333
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Recent Advances in Stochastic Gradient Descent in Deep Learning
2023 · Yingjie Tian, Yuqi Zhang, et al. · Mathematics · 278
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Convergence of the RMSProp deep learning method with penalty for nonconvex optimization
2021 · Dongpo Xu, Shengdong Zhang, et al. · Neural Networks · 232
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
2024 · Xingyu Xie, Pan Zhou, et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 224
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A survey of techniques for optimizing deep learning on GPUs
2019 · Sparsh Mittal, Shraiysh Vaishay · Journal of Systems Architecture · 197
Simulated Annealing Algorithm for Deep Learning
2015 · L.M. Rasdi Rere, Mohamad Ivan Fanany, et al. · Procedia Computer Science · 191
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Stochastic Subgradient Method Converges on Tame Functions
2019 · Damek Davis, Dmitriy Drusvyatskiy, et al. · Foundations of Computational Mathematics · 181
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Evolutionary deep learning: A survey
2022 · Zhi-Hui Zhan, Jian-Yu Li, et al. · Neurocomputing · 178
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
2018 · Ward, Rachel, Wu, Xiaoxia, et al. · arXiv (Cornell University) · 177
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Cell-type-directed design of synthetic enhancers
2023 · Ibrahim I. Taskiran, Katina I. Spanier, et al. · Nature · 174
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Revisiting Natural Gradient for Deep Networks
2014 · Razvan Pascanu, Yoshua Bengio · 159
An Exponential Learning Rate Schedule for Deep Learning
2019 · Li, Zhiyuan, Arora, Sanjeev · arXiv (Cornell University) · 143
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Simple Evolutionary Optimization Can Rival Stochastic Gradient Descent in Neural Networks
2016 · Gregory Morse, Kenneth O. Stanley · Genetic and Evolutionary Computation Conference (GECCO) · 120
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Calibrating the adaptive learning rate to improve convergence of ADAM
2022 · Qianqian Tong, Guannan Liang, et al. · Neurocomputing · 95
Geometry of Optimization and Implicit Regularization in Deep Learning
2017 · Neyshabur, Behnam, Tomioka, Ryota, et al. · arXiv (Cornell University) · 84
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Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
2017 · Balles, Lukas, Hennig, Philipp · arXiv (Cornell University) · 82
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On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
2018 · Zhou, Dongruo, Chen, Jinghui, et al. · arXiv (Cornell University) · 73
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Demand Side Control for Energy Saving in Renewable Energy Resources Using Deep Learning Optimization
2023 · Himanshu Shekhar, Chandra Bhushan Mahato, et al. · Electric Power Components and Systems · 71
Long-term planning of integrated local energy systems using deep learning algorithms
2021 · Saman Taheri, Mohammad Jooshaki, et al. · International Journal of Electrical Power & Energy Systems · 68
Stock Price Prediction using Technical Indicators: A Predictive Model using Optimal Deep Learning
2019 · Phd Scholar Computer Science & Engineering UIT, RGPV, Bhopal, (M.P.) India., sManish Agrawal, et al. · International Journal of Recent Technology and Engineering (IJRTE) · 68
Robust Stochastic Gradient Descent With Student-t Distribution Based First-Order Momentum
2020 · Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, et al. · IEEE Transactions on Neural Networks and Learning Systems · 65
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On the convergence of single-call stochastic extra-gradient methods
2019 · Hsieh, Yu-Guan, Iutzeler, Franck, et al. · arXiv (Cornell University) · 65
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Works count: 862
Citation count: 10,150
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