论文标题
深度自动编码器的分布式演变
Distributed Evolution of Deep Autoencoders
论文作者
论文摘要
自动编码器在从功能选择到信息检索的领域中取得了广泛的成功。尽管取得了成功，但由于缺乏对编码器和解码器如何影响自动编码器的整体性能，为给定任务设计一个自动编码器仍然是一项具有挑战性的事业。在这项工作中，我们提出了一个分布式系统，该系统使用有效的进化算法来设计模块化自动编码器。我们证明了该系统对多种学习和图像denoising任务的有效性。该系统在两个任务上都几乎将随机搜索量几乎按数量级，同时随着额外的工作节点的添加到系统中。
Autoencoders have seen wide success in domains ranging from feature selection to information retrieval. Despite this success, designing an autoencoder for a given task remains a challenging undertaking due to the lack of firm intuition on how the backing neural network architectures of the encoder and decoder impact the overall performance of the autoencoder. In this work we present a distributed system that uses an efficient evolutionary algorithm to design a modular autoencoder. We demonstrate the effectiveness of this system on the tasks of manifold learning and image denoising. The system beats random search by nearly an order of magnitude on both tasks while achieving near linear horizontal scaling as additional worker nodes are added to the system.
