Machine learning research has advanced in multiple aspects, including model structures and learning methods.
The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks—or similarly restrictive search spaces.
Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks.
We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space.
Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest,
e.g. CIFAR-10 variants, where modern techniques emerge in the top algorithms,
AutoML is a popular machine learning tool. it is open source. it is designed to automate the processes of developing algorithms for machine learning(ML). the current public version used manually created algorithms, which are fed into the system and finetuned automatically, by fiddling with new parameters.