Adaptive Batch Size

Adaptive batch size techniques in training deep neural networks aim to optimize the number of samples processed per gradient update, dynamically adjusting it throughout the training process to improve efficiency and generalization. Current research focuses on developing algorithms that adapt batch size based on factors like gradient variance, data heterogeneity, and available computational resources, often integrated with adaptive gradient methods like AdaGrad or within distributed training frameworks. These advancements offer significant potential for accelerating training, enhancing model performance, and improving resource utilization in various machine learning applications, particularly in large-scale and resource-constrained settings.