EDEN：用于高效机器学习的进化深度网络
EDEN: Evolutionary Deep Networks for Efficient Machine Learning
摘要：
随着深度的增加，深度神经网络继续表现出更好的性能，这是一个令人鼓舞的趋势，意味着网络架构和超参数的可能排列呈爆炸式增长，而对此几乎没有直观的指导。为了解决这种日益增加的复杂性，我们提出了进化深度网络（EDEN），这是一种计算效率高的神经进化算法，可以与任何深度神经网络平台（如TensorFlow）接口。我们表明，EDEN发展了简单而成功的架构，这些架构是从嵌入、1D和2D卷积、最大池化和全连接层及其超参数构建的。EDEN在七个图像和情感分类数据集上的评估表明，它可靠地找到了良好的网络，并在三种情况下实现了最先进的结果，即使在单个GPU上，也只需6-24小时。我们的研究首次尝试将神经进化应用于创建用于情感分析的1D卷积网络，包括优化嵌入层。
英文摘要：
Deep neural networks continue to show improved performance with increasing depth, an encouraging trend that implies an explosion in the possible permutations of network architectures and hyperparameters for which there is little intuitive guidance. To address this increasing complexity, we propose Evolutionary DEep Networks (EDEN), a computationally efficient neuro-evolutionary algorithm which interfaces to any deep neural network platform, such as TensorFlow. We show that EDEN evolves simple yet successful architectures built from embedding, 1D and 2D convolutional, max pooling and fully connected layers along with their hyperparameters. Evaluation of EDEN across seven image and sentiment classification datasets shows that it reliably finds good networks -- and in three cases achieves state-of-the-art results -- even on a single GPU, in just 6-24 hours. Our study provides a first attempt at applying neuro-evolution to the creation of 1D convolutional networks for sentiment analysis including the optimisation of the embedding layer.
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