论文标题
自动图表表格：多效金属性，可高效且稳健
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
论文作者
论文摘要
尽管早期的汽车框架着重于优化传统的ML管道及其超参数，但最新的汽车趋势是专注于神经体系结构搜索。在本文中，我们介绍了自动Pytorch，通过共同且稳健地优化网络架构和训练超级参数以实现完全自动化的深度学习（AUTODL），从而将这两个世界中的最佳融合在一起。通过将多保真优化与投资组合构造相结合，以进行热门神经网络（DNNS）（DNNS）和公共基准以用于表格数据，自动托管通过将多保真优化与投资组合结合结合在一起，在几个表格基准上实现了最先进的性能。为了彻底研究如何设计这样的自动模式的假设，我们还为DNN的学习曲线提供了新的基准，称为LCBench，并对典型的Auto-Pytorch进行了广泛的消融研究，以典型的AutoMll基准测试，最终表明Auto-Pytorch在The The The The-Art-Art-Art-Art-Art-Art-Art-Art-Art-Art-Art-Art-Arter上的表现都更好。
While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this paper, we introduce Auto-PyTorch, which brings the best of these two worlds together by jointly and robustly optimizing the architecture of networks and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch achieves state-of-the-art performance on several tabular benchmarks by combining multi-fidelity optimization with portfolio construction for warmstarting and ensembling of deep neural networks (DNNs) and common baselines for tabular data. To thoroughly study our assumptions on how to design such an AutoDL system, we additionally introduce a new benchmark on learning curves for DNNs, dubbed LCBench, and run extensive ablation studies of the full Auto-PyTorch on typical AutoML benchmarks, eventually showing that Auto-PyTorch performs better than several state-of-the-art competitors on average.
