Low gradient sampling
Low gradient sampling是一种用于优化的随机梯度下降算法变体,其中样本被选择以最小化其梯度范数的加权和,从而有助于减少梯度中的噪声和提高收敛速度。以下是一些与此主题相关的论文和Python代码示例:
论文:“Stochastic Gradient Descent with Restart and Averaging” by Jeremy Howard and Sebastian Ruder Code: https://github.com/fastai/fastai/blob/master/fastai/optimizers.py\#L168
论文:“Accelerating Stochastic Gradient Descent using Predictive Variance Reduction” by Jian Zhang, Ioannis Mitliagkas, and Christopher Ré Code: https://github.com/jianzhangcs/PVR\_SGD
论文:“On the Variance of the Adaptive Learning Rate and Beyond” by Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar Code: https://github.com/boulanni/theano-nets/blob/master/nets/sgd.py
这些代码示例可以帮助您理解如何在PyTorch、TensorFlow或其他深度学习框架中使用low gradient sampling算法进行优化。
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