Scaling Strategy

Scaling strategies in machine learning aim to efficiently increase model size and performance, addressing the growing computational demands of large models. Current research focuses on optimizing training processes, including techniques like knowledge transfer from smaller models (e.g., using scale-up and scale-out approaches) and improved training methodologies (e.g., enhanced data augmentation and optimization). This work is crucial for advancing various applications, from natural language processing and autonomous driving (using architectures like MoE and MapNeXt) to resource-constrained domains such as on-device speech processing (exploring Conv-Tasnet scaling), ultimately enabling the development of more powerful and efficient AI systems.