2016年9月15日 · Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are …

2016年8月13日 · In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We …

2017年11月29日 · Stochastic Gradient Descent (SGD) is the method of choice for large scale problems, most notably in deep learning. Recent studies target improving convergence and …

2025年1月14日 · In this paper we analyze the behaviour of the stochastic gradient descent (SGD), a widely used method in supervised learning for optimizing neural network weights via …

批量梯度下降(Batch gradient descent)、随机梯度下降(SGD)、小批量梯度下降(Mini-batch gradient descent) 其实都是一回事，区别在于对多少样本数量计算梯度，BGD是对所有样本计 …

2023年1月29日 · Among machine learning models, stochastic gradient descent (SGD) is not only simple but also very effective. This study provides a detailed analysis of contemporary state-of …

2019年2月12日 · Stochastic gradient descent (SGD) is a fundamental algorithm which has had a profound impact on machine learning. This article surveys some important results on SGD and …

Stochastic Gradient Descent is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. …

Multi-epoch, small-batch, Stochastic Gradient Descent (SGD) has been the method of choice for learning with large over-parameterized models. A popular theory for explaining why SGD …

This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm’s convergence properties in non-convex problems. We first show that the sequence …