2018年7月13日 · Abstract: Stochastic Gradient Descent (SGD) based training of neural networks with a large learning rate or a small batch-size typically ends in well-generalizing, flat regions …

2025年3月3日 · Stochastic Gradient Descent (SGD) is an efficient optimization algorithm for large datasets in machine learning, utilizing random data points for faster convergence and …

Stochastic Gradient Descent (SGD) simply does away with the expectation in the update and computes the gradient of the parameters using only a single or a few training examples. The …

2020年4月10日 · Stochastic gradient descent (SGD) SGD calculates the gradients with one data. The calculation becomes faster, but the process of gradient descent becomes fluctuating.

2022年1月10日 · We propose LoCal+SGD, a new algorithmic approach to accelerate DNN training by selectively combining localized or Hebbian learning within a Stochastic Gradient …

2018年12月20日 · Overall, our results show that the SGD dynamics in the subspace of the sharpest directions influence the regions that SGD steers to (where larger learning rate or …

2022年9月14日 · SSD-SGD is a general algorithm proposed for distributed DNN training acceleration via communication sparsification, which combines the merits of SSGD and …

2020年9月5日 · We devise a new weight-noise injection-based SGD method that adds symmetrical noises to the DNN weights. The training with symmetrical noise evaluates the loss …

一个常用于分布式设定中的训练的常用算法是随机梯度下降（sgd），该算法将是我们进一步讨论中的核心点。 需要指出一个重点，针对 SGD 提及的原则可以轻松地移植给其它常用的优化算 …

d with Stochastic Gradient Descent (SGD). While understanding the generalization capability of DNNs remains an open challenge, it has been hypothesized that SGD acts as an implicit …