Automated and adaptive design and training of neural networks

This describes a system for automatically designing neural networks. It works by selecting the appropriate mathematical formula (loss function) and output method (activation function) for the network. This choice is made based on the type of prediction problem it needs to solve, such as regression or classification, and the characteristics of the data it will learn from, like whether the target variable is zero-inflated or follows a Poisson distribution.

Why it matters: The rise of diverse AI applications and complex datasets since 2021 has amplified the need for automated machine learning (AutoML). This invention's adaptive approach to neural network design, particularly loss function selection, is more relevant for handling today's varied data challenges.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedMachine learning software implementation

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