Mini Batch

Mini-batching is a core technique in training machine learning models, aiming to optimize the trade-off between computational efficiency and the accuracy of gradient estimations. Current research focuses on understanding the impact of mini-batch size on algorithm convergence, generalization performance, and robustness, exploring various scheduling strategies (e.g., increasing batch size with learning rate adjustments) and their effects across different model architectures (including deep neural networks, graph neural networks, and reinforcement learning agents). These investigations are crucial for improving the scalability and efficiency of training large models, impacting diverse applications from image recognition and natural language processing to resource-constrained distributed learning environments.