Automatic Batching

Automatic batching optimizes the processing of data in various machine learning applications by grouping individual items into batches for more efficient computation. Current research focuses on improving batching strategies for diverse tasks, including training large language models (using techniques like mixture-of-experts and dynamic batching), graph neural networks (employing distributed training and novel sampling methods), and deep learning models for image segmentation (addressing class imbalances and under-represented samples). These advancements enhance the speed, resource efficiency, and scalability of machine learning, impacting fields ranging from manufacturing optimization to biomedical image analysis.