Neural Architecture Search using Particle Swarm and Ant Colony Optimization

arXiv:2403.03781v1 Announce Type: cross
Abstract: Neural network models have a number of hyperparameters that must be chosen along with their architecture. This can be a heavy burden on a novice user, choosing which architecture and what values to assign to parameters. In most cases, default hyperparameters and architectures are used. Significant improvements to model accuracy can be achieved through the evaluation of multiple architectures. A process known as Neural Architecture Search (NAS) may be applied to automatically evaluate a large …

abstract ant architecture architectures arxiv cases colony cs.ai cs.lg cs.ne network neural architecture search neural network optimization parameters particle search type values