论文标题
基于模型的异步超级参数和神经体系结构搜索
Model-based Asynchronous Hyperparameter and Neural Architecture Search
论文作者
论文摘要
我们介绍了一种基于模型的异步多效率方法，用于超参数和神经体系结构搜索，该方法结合了异步超频带和基于高斯过程的贝叶斯优化的优势。我们方法的核心是一个概率模型，可以同时推理超参数和资源水平，并在存在待处理评估的情况下支持决策。我们证明了我们的方法在各种挑战性基准，对于表格数据，图像分类和语言建模方面的有效性，并报告了对当前最新方法的实质性加速。我们的新方法以及异步基线在分布式框架中实施，该框架将与本出版物一起开放。
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization. At the heart of our method is a probabilistic model that can simultaneously reason across hyperparameters and resource levels, and supports decision-making in the presence of pending evaluations. We demonstrate the effectiveness of our method on a wide range of challenging benchmarks, for tabular data, image classification and language modelling, and report substantial speed-ups over current state-of-the-art methods. Our new methods, along with asynchronous baselines, are implemented in a distributed framework which will be open sourced along with this publication.
