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- W2810294523 abstract "Deep neural networks (DNNs) have proven to be effective at solving challenging problems, but their success relies on finding a good architecture to fit the task. Designing a DNN requires expert knowledge and a lot of trial and error, especially as the difficulty of the problem grows. This paper proposes a fully automatic method with the goal of optimizing DNN topologies through memetic evolution. By recasting the mutation step as a series of progressively refined educated local-search moves, this method achieves results comparable to best human designs. Our extensive experimental study showed that the proposed memetic algorithm supports building a real-world solution for segmenting medical images, it exhibits very promising results over a challenging CIFAR-10 benchmark, and works very fast. Given the ever growing availability of data, our memetic algorithm is a very promising avenue for hands-free DNN architecture design to tackle emerging classification tasks." @default.
- W2810294523 created "2018-07-10" @default.
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- W2810294523 date "2018-07-02" @default.
- W2810294523 modified "2023-10-17" @default.
- W2810294523 title "Memetic evolution of deep neural networks" @default.
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- W2810294523 doi "https://doi.org/10.1145/3205455.3205631" @default.
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