Summary
Training modern artificial intelligence systems requires large amounts of computing time, energy, and money. Many of the optimization methods used to train neural networks are still chosen largely through trial and error because existing theory does not adequately explain why some methods work better than others on different model architectures. This project will develop a scientific foundation for making training faster, more reliable, and more resource efficient by linking optimization methods to the structure of the neural networks they are used to train. The project can reduce the cost and
