图 书 馆
Auto-Keras：一种高效的神经结构搜索系统Auto-Keras: An Efficient Neural Architecture Search System |
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| 课程网址: | http://videolectures.net/kdd2019_jin_song_hu/ |
| 主讲教师: | Haifeng Jin |
| 开课单位: | 得克萨斯农工大学 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Neural architecture search (NAS) has been proposed to automatically tune deep neural networks, but existing search algorithms, e.g., NASNet, PNAS, usually suffer from expensive computational cost. Network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling more efficient training during the search. In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search. The framework develops a neural network kernel and a tree-structured acquisition function optimization algorithm to efficiently explores the search space. Extensive experiments on real-world benchmark datasets have been done to demonstrate the superior performance of the developed framework over the state-of-the-art methods. Moreover, we build an open-source AutoML system based on our method, namely Auto-Keras. The code and documentation are available at autokeras. The system runs in parallel on CPU and GPU, with an adaptive search strategy for different GPU memory limits. |
| 关 键 词: | Auto-Keras; 大数据科学; 神经结构搜索系统 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-09-16：cyh |
| 最后编审: | 2022-09-19：cyh |
| 阅读次数: | 64 |