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 hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use.

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 speed of the SGD algorithm. In this paper, we equip the SGD algorithm and its advanced versions with an intriguing feature, namely handling constrained problems.

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 empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively.

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 a minimization of non-convex loss functions.

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是对所有样本计算梯度（一次性传入所有样本，计算损失函数，进而计算梯度），理论上可以找到全局最优解 ...

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. That is for weights w and a loss function L we have: w t + 1 = w t …

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 its variants that arose in machine learning.

2022年3月8日 · 无损的parallel SGD就是一种mini batch，因此将👆定理换一种写法（其实就是写成不等式右边趋于零时），得到收敛所需迭代次数与方差的关系. 通常第一项会主导迭代次数，因此结论：收敛所需的迭代次数与随机梯度的二阶方差界 \(B\) 成线性关系. 随机量化与编码 ...

2022年8月1日 · In this paper we pursued a truly data driven approach to the problem of getting a potentially deeper understanding of the high-dimensional parameter loss surface, and the landscape traced out by SGD, in the context of fitting (deep) neural networks to real data sets and by analyzing the data generated through SGD in order to possibly discovery ...

2020年9月18日 · SGD算法可以加快计算的过程,但无法加快通信过程.在分布式机器学习环境中,通信问题是限制其发展的关键因素. 为了解决这个问题,可以对求解出来的梯度进行压缩操作,使用comp(g)comp(\mathbf{g})comp(g)来代