Improving Neural Architecture Search Image Classifiers via Ensemble Learning by Vladimir Macko, Charles Weill, Hanna Mazzawi and Javier Gonzalvo
AdaNAS is an algorithm for learning an ensemble that improves the performance of neural architecture search models while having a similar parameter count as single large model. Our experiments demonstrate that these ensembles improve accuracy upon a single neural network of the same size. Our models achieve comparable results with the state-of-the-art on CIFAR-10 and set a new state-of-the-art on CIFAR-100.
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email.
Paris Machine Learning: Meetup.com||@Archives||LinkedIn||Facebook|| @ParisMLGroup< br/> About LightOn: Newsletter ||@LightOnIO|| on LinkedIn || on CrunchBase || our Blog
About myself: LightOn || Google Scholar || LinkedIn ||@IgorCarron ||Homepage||ArXiv