This describes a way to automatically build a neural network that performs well. It starts with a basic network and repeatedly modifies it by trying out different changes from a predefined set of options. Each modified network is then trained, and if it meets certain performance goals, it becomes the final, optimized network; otherwise, the process continues with further modifications until a suitable network is found.
Why it matters: Filed before advanced AutoML and Neural Architecture Search became mainstream. The method's iterative network design is now more efficient and relevant given current AI model complexity and computational resources.
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