Results 11 to 20 of about 1,424,164 (189)
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
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Stochastic Modified Flows for Riemannian Stochastic Gradient Descent
SIAM Journal on Control and OptimizationAbstract. We give quantitative estimates for the rate of convergence of Riemannian stochastic gradient descent (RSGD) to Riemannian gradient flow and to a diffusion process, the so-called Riemannian stochastic modified flow (RSMF). Using tools from stochastic differential geometry, we show that, in the small learning rate regime, RSGD
Benjamin Gess +2 more
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Dual Stochastic Natural Gradient Descent
CoRR, 2020 [EN] Although theoretically appealing, Stochastic Natural Gradient Descent (SNGD) is computationally expensive, it has been shown to be highly sensitive to the learning rate, and it is not guaranteed to be convergent. Convergent Stochastic Natural Gradient Descent (CSNGD) aims at solving the last two problems.
Sánchez-López, Borja +1 more
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Communication-efficient stochastic gradient descent, with applications to neural networks [PDF]
, 2017 Parallel implementations of stochastic gradient descent (SGD) have received significant research attention, thanks to its excellent scalability properties.
Vojnovic, Milan +4 more
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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
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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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