Created by W.Langdon from gp-bibliography.bib Revision:1.7185
AutoML-Zero, the proposal to automatically search forML algorithms from scratch with minimal human design
A novel framework with open-sourced code1and a searchspace that combines only basic mathematical operations
Detailed results to show potential through the discoveryof nuanced ML algorithms using evolutionary search.
CIFAR-10, SVHN, ImageNet, Fashion MNIST
memory scalar, vector and matrix variables (e.g.s1,v1,m1)
Throughput of 2000 to 10000 algorithms/second/cpu core. functional equivalence checking. hurdles (ICML 2019). Comparison with random search: 'As the tasktype becomes more difficult, evolution vastly outperforms RS'
Blog: (Thursday, July 9, 2020) https://ai.googleblog.com/2020/07/automl-zero-evolving-code-that-learns.html 'Evolving Learning Algorithms from Scratch We use a variant of classic evolutionary methods to search the space of algorithms. These methods have proved useful in discovering computer programs since the 80s. Their simplicity and scalability makes them especially suitable for the discovery of learning algorithms.' https://en.wikipedia.org/wiki/Genetic_programming
Google Brain team",
Genetic Programming entries for Esteban Real Chen Liang David So Quoc V Le