TabPack让你不用费心调参就能达到好效果，在普通笔记本上跑都比别人用GPU调参快。
TabPack是一种针对表格数据的高效MLP集成方法，在单次运行中并行采样并训练多个不同超参数的MLP，并在训练过程中动态选择集成成员。在多个中大型公开数据集上，TabPack的默认设置性能与经过大量调优的先前方法相当。运行TabPack默认配置在一台MacBook上所需时间少于在工业级GPU上调优某些基线的时间。
TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning
In deep learning for tabular data, efficient ensembles of multilayer perceptrons (MLPs) have recently emerged as effective and practical architectures. Existing methods of this kind use the same hyperparameters for all underlying MLPs, which requires hyperparameter tuning for achieving the best performance. In this work, we introduce TabPack, an efficient MLP ensemble with strong out-of-the-box performance and reduced reliance on traditional tuning. In a single run, TabPack samples and trains many MLPs with different hyperparameters efficiently in parallel and selects ensemble members on the fly during training. Thus, TabPack only requires specifying ranges from which to sample MLP hyperparameter rather than exact hyperparameter values, which naturally demands less precision for good performance. In experiments on medium-to-large public datasets, TabPack with default settings performs on par with extensively tuned prior methods, thus substantially reducing effort and compute resources needed to achieve competitive results on tabular tasks. Notably, running the default TabPack configuration on a modern MacBook took less time than tuning some baselines on an industry-grade GPU.