Minibatch Stochastic

Minibatch stochastic methods are optimization techniques that use small random subsets of data to update model parameters, improving efficiency in training large models like deep neural networks. Current research focuses on addressing challenges such as the generalization gap (where large batch sizes improve training speed but hurt generalization), communication overhead in distributed training, and the efficient handling of non-independent data samples, particularly in graph convolutional networks. These advancements are crucial for scaling machine learning to massive datasets and improving the performance and robustness of models across various applications, including image classification, generative modeling, and reinforcement learning.