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[D] Neural Architecture Search

Recently, Neural Architecture Search is coming back to the research spotlight. Elsken et al. published a survey on this topic (https://arxiv.org/pdf/1808.05377.pdf ) but the development is fast and many new works are emerging. For example, there is Weight Agnostic Neural Network (WANN) https://arxiv.org/abs/1906.04358 that demonstrates that Neural Architectures can be more significant than the weights of the network. You can read of the list of paper in this topic at https://www.automl.org/automl/literature-on-neural-architecture-search/ . Nevertheless, this type of topic is already researched in 1990 ( https://pdfs.semanticscholar.org/2118/55f1de279c452858177331860cbc326351ab.pdf ), are there still significance in improvement? If so, how much?

Are researchers just making up new Neural Architecture Search methods for publication, or is there really a big difference? Are there any work that focused on a detailed comparison for Neural Architecture Search.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.