Results 1 to 10 of about 28,952 (292)
Stochastic gradient descent for optimization for nuclear systems [PDF]
Scientific Reports, 2023 The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature.
Austin Williams +5 more
doaj +3 more sources
On the different regimes of stochastic gradient descent [PDF]
Proceedings of the National Academy of Sciences of the United States of AmericaModern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch sizeB, and the step size or learning rateη. For smallBand largeη, SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the “temperature”T≡η/B.
Antonio Sclocchi +2 more
exaly +5 more sources
Stabilizing updates in differentially private stochastic gradient descent with buffered rejection [PDF]
Scientific ReportsDifferentially private stochastic gradient descent is a standard algorithm for training deep models on sensitive data, but under tight privacy budgets it must add large noise to every step, which slows convergence and reduces accuracy.
Sifan Deng +4 more
doaj +2 more sources
Stochastic Gradient Descent for Kernel-Based Maximum Correntropy Criterion [PDF]
EntropyMaximum correntropy criterion (MCC) has been an important method in machine learning and signal processing communities since it was successfully applied in various non-Gaussian noise scenarios.
Tiankai Li +3 more
doaj +2 more sources
Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System [PDF]
Sensors, 2020 The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem.
Nam D. Vo, Minsung Hong, Jason J. Jung
doaj +2 more sources
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
doaj +3 more sources
Emergent universal long-range structure in random-organizing systems [PDF]
Nature CommunicationsSelf-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges from local noisy dynamics remains poorly understood.
Satyam Anand +2 more
doaj +2 more sources
A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent [PDF]
IEEE Transactions on Automatic Control, 2022 Shi Pu +2 more
exaly +2 more sources
Stochastic gradient descent for wind farm optimization [PDF]
Wind Energy Science, 2023 It is important to optimize wind turbine positions to mitigate potential wake losses. To perform this optimization, atmospheric conditions, such as the inflow speed and direction, are assigned probability distributions according to measured data, which ...
J. Quick +4 more
doaj +1 more source
AG-SGD: Angle-Based Stochastic Gradient Descent
IEEE Access, 2021 In the field of neural network, stochastic gradient descent is often employed as an effective method of accelerating the result's convergence. Generating the new gradient from the past gradient is a common method adopted by many existing optimization ...
Chongya Song, Alexander Pons, Kang Yen
doaj +1 more source