Machine Learning

DENSER: Deep Evolutionary Network Structured Representation

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  • arXiv
    5 pts

    DENSER: Deep Evolutionary Network Structured Representation

    Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation (EC). The algorithm not only searches for the best network topology (e.g., number of layers, type of layers), but also tunes hyper-parameters, such as, learning parameters or data augmentation parameters. The automatic design is achieved using a representation with two distinct levels, where the outer level encodes the general structure of the network, i.e., the sequence of layers, and the inner level encodes the parameters associated with each layer. The allowed layers and hyper-parameter value ranges are defined by means of a human-readable Context-Free Grammar. DENSER was used to evolve ANNs for two widely used image classification benchmarks obtaining an average accuracy result of up to 94.27% on the CIFAR-10 dataset, and of 78.75% on the CIFAR-100. To the best of our knowledge, our CIFAR-100 results are the highest performing models generated by methods that aim at the automatic design of Convolutional Neural Networks (CNNs), and is amongst the best for manually designed and fine-tuned CNNs .

    DENSER: Deep Evolutionary Network Structured Representation
    by Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro
    https://arxiv.org/pdf/1801.01563v1.pdf

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