  Learned embeddings are widely used to obtain concise data representation and
enable transfer learning between different data sets and tasks. In this paper,
we present Silhouette, our approach that leverages publicly-available
performance data sets to learn CPU embeddings. We show how these embeddings
enable transfer learning between data sets of different types and sizes. Each
of these scenarios leads to an improvement in accuracy for the target data set.
