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They picked such a horrible name for this. Yes it's catchy but please understand what it actually is. You have every ML vendor (disclaimer: My competitors) fol
by agibsonccc 9y ago
They picked such a horrible name for this. Yes it's catchy but please understand what it actually is.
You have every ML vendor (disclaimer: My competitors) following google with this name trying to get a piece of the hype pie spreading more confusion in their marketing material. It drives me nuts.
You have folks doing everything from claiming hyper param search is "automl" to transfer learning + grid search + "insert random architecture search" here is magic that will save us all from needing to understand how this stuff works. People it takes more than that. Please read the papers for yourself down below and try to understand the limitations of these techniques.
Granted, it's great that we are attempting this, it's a real step forward, but please call it for what it is.
Here's the papers referenced in the original blog post(https://www.blog.google/topics/google-cloud/cloud-automl-making-ai-accessible-every-business/ https://www.blog.google/topics/google-cloud/cloud-automl-mak...) :
Learning Transferable Architectures for Scalable Image Recognition, Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. Arxiv, 2017.
Progressive Neural Architecture Search, Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy, Arxiv, 2017.
Large-Scale Evolution of Image Classifiers, Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Quoc Le, Alex Kurakin. International Conference on Machine Learning, 2017.
Neural Architecture Search with Reinforcement Learning, Barret Zoph, Quoc V. Le. International Conference on Learning Representations, 2017.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi. AAAI, 2017.
Bayesian Optimization for a Better Dessert, Benjamin Solnik, Daniel Golovin, Greg Kochanski, John Elliot Karro, Subhodeep Moitra, D. Sculley. NIPS, Workshop on Bayesian Optimization, 2017.