Results 21 to 30 of about 22,588 (154)
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
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A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent [PDF]
IEEE Transactions on Automatic Control, 2022 Ioannis Paschalidis +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
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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
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Stochastic Reweighted Gradient Descent
CoRR, 2021 Despite the strong theoretical guarantees that variance-reduced finite-sum optimization algorithms enjoy, their applicability remains limited to cases where the memory overhead they introduce (SAG/SAGA), or the periodic full gradient computation they require (SVRG/SARAH) are manageable.
Ayoub El Hanchi, David A. Stephens
openaire +2 more sources
IET Control Theory & Applications, 2021 Stochastic gradient descent algorithm is a classical and useful method for stochastic optimisation. While stochastic gradient descent has been theoretically investigated for decades and successfully applied in machine learning such as training of deep ...
Xiaoxue Geng, Gao Huang, Wenxiao Zhao
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Training a Two-Layer ReLU Network Analytically
Sensors, 2023 Neural networks are usually trained with different variants of gradient descent-based optimization algorithms such as the stochastic gradient descent or the Adam optimizer.
Adrian Barbu
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Stochastic gradient descent on GPUs [PDF]
Proceedings of the 8th Workshop on General Purpose Processing using GPUs, 2015 Irregular algorithms such as Stochastic Gradient Descent (SGD) can benefit from the massive parallelism available on GPUs. However, unlike in data-parallel algorithms, synchronization patterns in SGD are quite complex. Furthermore, scheduling for scale-free graphs is challenging.
Rashid Kaleem +2 more
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Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization
Fractal and Fractional, 2022 Stochastic gradient descent is the method of choice for solving large-scale optimization problems in machine learning. However, the question of how to effectively select the step-sizes in stochastic gradient descent methods is challenging, and can ...
Ruijuan Chen, Xiaoquan Tang, Xiuting Li
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Stochastic gradient descent for hybrid quantum-classical optimization [PDF]
Quantum, 2020 Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of parameterized quantum circuits. In this work, we explore the consequences
Ryan Sweke +6 more
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