论文标题
轮廓：朝向性能和可转移的CPU嵌入
Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings
论文作者
论文摘要
学到的嵌入方式被广泛用于获得简洁的数据表示，并在不同的数据集和任务之间进行转移学习。在本文中，我们介绍了轮廓，我们的方法利用了公开可用的性能数据集来学习CPU嵌入。我们展示了这些嵌入方式如何在不同类型和大小的数据集之间进行转移学习。这些方案中的每一个都可以提高目标数据集的准确性。
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.
