This paper provides a unified description of Widening, a framework for the use of parallel (or otherwise abundant) computational resources to improve model quality. We discuss different theoretical approaches to Widening with and without consideration of diversity. We then soften some of the underlying constraints so that Widening can be implemented in real world algorithms. We summarize earlier experimental results demonstrating the potential impact as well as promising implementation strategies before concluding with a survey of related work.

中文翻译：

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扩展：使用并行资源来提高模型质量

本文提供了Widening的统一描述，该框架是使用并行（或其他方式丰富的）计算资源来提高模型质量的框架。我们讨论了在不考虑多样性的情况下拓宽的不同理论方法。然后，我们软化一些基本约束，以便可以在现实世界的算法中实现扩展。在总结相关工作之前，我们总结了较早的实验结果，这些结果表明了潜在的影响以及有希望的实施策略。