Results 11 to 20 of about 23,797 (211)

Stochastic gradient descent for optimization for nuclear systems. [PDF]

Sci Rep, 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.
Williams A, Walton N, Maryanski A, Bogetic S, Hines W, Sobes V.   +5 more
europepmc   +2 more sources

Stabilizing updates in differentially private stochastic gradient descent with buffered rejection. [PDF]

Sci Rep
Differentially 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.
Deng S, Zhang K, Zhang W, Jiang H, Tsai PW.   +4 more
europepmc   +2 more sources

Stochastic Gradient Descent for Kernel-Based Maximum Correntropy Criterion. [PDF]

Entropy (Basel)
Maximum 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.
Li T, Wang B, Peng C, Yin H.
europepmc   +2 more sources

Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System. [PDF]

Sensors (Basel), 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.
Vo ND, Hong M, Jung JJ.
europepmc   +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
exaly   +3 more sources

A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent. [PDF]

IEEE Trans Automat Contr, 2022
Pu S, Olshevsky A, Paschalidis IC.
europepmc   +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, P.-E. Rethore, M. Mølgaard Pedersen, R. V. Rodrigues, M. Friis-Møller   +4 more
doaj   +1 more source

On the different regimes of stochastic gradient descent. [PDF]

Proc Natl Acad Sci U S A
Sclocchi A, Wyart M.
europepmc   +2 more sources

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

Almost sure convergence of randomised‐difference descent algorithm for stochastic convex optimisation

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
doaj   +1 more source