Suchergebnisse - standard stochastic gradient algorithm

Particle filtering-based recursive identification for controlled auto-regressive systems with quantised output von Ding, Jie, Chen, Jiazhong, Lin, Jinxing, Jiang, Guoping

“… In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm …”
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Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points von Balasubramanian, Krishnakumar, Ghadimi, Saeed

“… to the standard stochastic gradient algorithm using only zeroth-order information. To facilitate zeroth-order optimization in high dimensions, we explore the advantages of structural sparsity assumptions. Specifically …”
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Convergence Analysis of Weighted Stochastic Gradient Identification Algorithms Based on Latest‐Estimation for ARX Models von Wu, Ai‐Guo, Fu, Fang‐Zhou, Dong, Rui‐Qi

“… In this paper, weighted stochastic gradient (WSG) algorithms for ARX models are proposed by modifying the standard stochastic gradient identification algorithms …”
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Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems von Feng Ding, Guangjun Liu, Liu, Xiaoping Peter

“… ) algorithm is proposed to estimate the model parameters with high computational efficiency compared with the standard stochastic gradient (SG) algorithm …”
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A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model von Li, Jinli, Yuan, Ye, Ruan, Tao, Chen, Jia, Luo, Xin

“… ) is frequently adopted as the learning algorithm. However, a standard SGD algorithm updates a decision parameter with the stochastic gradient on the instant loss only, without considering information described by prior updates …”
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Stochastic Gradient Markov Chain Monte Carlo von Nemeth, Christopher, Fearnhead, Paul

“… In this article, we focus on a particular class of scalable Monte Carlo algorithms, stochastic gradient Markov chain Monte Carlo (SGMCMC …”
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Almost sure convergence rates of stochastic proximal gradient descent algorithm von Liang, Yuqing, Xu, Dongpo

“… Stochastic proximal gradient descent (Prox-SGD) is a standard optimization algorithm for solving stochastic composite optimization problems in machine learning …”
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Guided Stochastic Gradient Descent Algorithm for inconsistent datasets von Sharma, Anuraganand

“… Stochastic Gradient Descent (SGD) Algorithm, despite its simplicity, is considered an effective and default standard optimization algorithm for machine learning …”
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Differentially private stochastic gradient descent via compression and memorization von Phong, Le Trieu, Phuong, Tran Thi

“… Our differentially private algorithm, called dp-memSGD for short, converges mathematically at the same rate of 1/T as standard stochastic gradient descent (SGD …”
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Fastest rates for stochastic mirror descent methods von Hanzely, Filip, Richtárik, Peter

“… We propose and analyze two new algorithms: Relative Randomized Coordinate Descent (relRCD) and Relative Stochastic Gradient Descent (relSGD …”
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Linear mixed effects models for non-Gaussian continuous repeated measurement data von Asar, Özgür, Bolin, David, Diggle, Peter J., Wallin, Jonas

“… A standard framework for analysing data of this kind is a linear Gaussian mixed effects model within which the outcome variable can be decomposed into fixed effects, time invariant and time-varying …”
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Adaptive Stochastic Gradient Descent Optimisation for Image Registration von Klein, Stefan, Pluim, Josien P. W., Staring, Marius, Viergever, Max A.

“… The proposed adaptive stochastic gradient descent (ASGD) method is compared to a standard, non-adaptive Robbins-Monro (RM) algorithm …”
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A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor Analysis von Li, Jinli, Luo, Xin, Yuan, Ye, Gao, Shangce

“… from them. However, a standard SGD algorithm updates a latent factor based on the current stochastic gradient only, without the considerations on the past information, making a resultant model suffer from slow convergence …”
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An Efficient Stochastic Gradient Descent Algorithm to Maximize the Coverage of Cellular Networks von Liu, Yaxi, Huangfu, Wei, Zhang, Haijun, Long, Keping

“… A standard gradient descent algorithm and its improved version, namely a Stochastic Gradient Descent (SGD …”
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A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor Analysis von Yuan, Ye, Li, Jinli, Luo, Xin

“… However, an SGD-based LFA model is often stacked by slow convergence since a standard SGD algorithm updates a single latent factor depending on the stochastic gradient of current instance learning …”
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Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms von Luo, Xin, Wang, Dexian, Zhou, MengChu, Yuan, Huaqiang

“… Stochastic gradient descent (SGD) is a highly efficient algorithm for building an LF model …”
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Model-free control of nonlinear stochastic systems with discrete-time measurements von Spall, J.C., Cristion, J.A.

“… This paper considers the use of the simultaneous perturbation stochastic approximation algorithm which requires only system measurements …”
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Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization von Mu, Yadong, Liu, Wei, Liu, Xiaobai, Fan, Wei

“… To improve the stability of stochastic gradient, recent years have witnessed the proposal of several semi-stochastic gradient descent algorithms, which distinguish themselves from standard SGD …”
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Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers von Li, Jinli, Yuan, Ye, Luo, Xin

“… Unfortunately, a standard SGD algorithm trains a single latent factor relying on the stochastic gradient related to the current learning error only, leading to a slow convergence rate …”
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Device Specifications for Neural Network Training with Analog Resistive Cross‐Point Arrays Using Tiki‐Taka Algorithms von Byun, Jinho, Kim, Seungkun, Kim, Doyoon, Lee, Jimin, Ji, Wonjae, Kim, Seyoung

“… Recently, specialized training algorithms for analog cross‐point array‐based neural network accelerators have been introduced to counteract device non …”
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