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• W2901595091 abstract "We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby the learning algorithm searches for effective architectures on the fly, while actively learning. We apply our strategy using three known querying techniques (softmax response, MC-dropout, and coresets) and show that the proposed approach overwhelmingly outperforms active learning using fixed architectures." @default.
• W2901595091 created "2018-11-29" @default.
• W2901595091 creator A5025669300 @default.
• W2901595091 creator A5061409282 @default.
• W2901595091 date "2018-11-19" @default.
• W2901595091 modified "2023-09-27" @default.
• W2901595091 title "Deep Active Learning with a Neural Architecture Search" @default.
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• W2901595091 hasPublicationYear "2018" @default.
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