方法证据记录
Stochastic Optimization
Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Stochastic Optimization (SGD and Variants)
分类方法记录 · process-pipeline / optimization
- Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407. · DOI 10.1214/aoms/1177729586
- Kingma, D.P. & Ba, J. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR 2015). · URL
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