A proper form of data characterization can guide the process of learning-algorithm selection and model-performance estimation. The field of meta-learning has provided a rich body of work describing effective forms of data characterization using different families of meta-features (statistical, model-based, information-theoretic, topological, etc.). In this paper, we start with the abundant set of existing meta-features and propose a method to induce new abstract meta-features as latent variables in a deep neural network. We discuss the pitfalls of using traditional meta-features directly and argue for the importance of learning high-level task properties. We demonstrate our methodology using a deep neural network as a feature extractor. We demonstrate that 1) induced meta-models mapping abstract meta-features to generalization performance outperform other methods by ~18% on average, and 2) abstract meta-features attain high feature-relevance scores.
翻译:适当的数据定性形式可以指导学习-算法选择和模型-性能估计过程。元学习领域提供了大量的工作内容,介绍了利用不同种类的元物(统计、模型、信息理论、地形等)来描述数据定性的有效形式。在本文中,我们从丰富的现有元物开始,提出在深层神经网络中将新的抽象元物作为潜在变数的方法。我们讨论了直接使用传统元物的缺陷,并主张学习高层次任务特性的重要性。我们用深层神经网络来展示我们的方法,作为特征提取器。我们证明:(1) 诱导元模型绘制抽象的元物图,将性优于其他方法,平均为~18%;和(2) 抽象元物取得高特征相关性分数。