  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.
