Faster Training

Faster training of machine learning models is a crucial research area aiming to reduce computational costs and energy consumption while maintaining or improving model performance. Current efforts focus on optimizing existing architectures like Transformers and neural networks through techniques such as improved initialization strategies, efficient sampling methods (e.g., importance sampling, subgraph sampling), and architectural modifications (e.g., lightweight models, early exits, masked transformers). These advancements are significant because they enable the training of larger, more complex models and the application of deep learning to resource-constrained environments and time-sensitive tasks, ultimately accelerating progress across various scientific fields and practical applications.