论文标题
旅行观察者模型：通过空间变量嵌入的多任务学习
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
论文作者
论文摘要
本文将一般预测系统构成了一个观察者围绕连续空间传播的观察者，在某些位置测量值并在其他位置进行预测。观察者对要解决的任何特定任务完全不可知。它仅关心测量位置及其值。这种观点导致了一个机器学习框架，在该框架中，可以通过将其输入和输出变量嵌入共享空间来通过单个模型来解决看似无关的任务。开发了框架的实现，其中这些变量嵌入与内部模型参数共同学习。在实验中，该方法显示为（1）在空间和时间中恢复变量的直观位置，（2）在具有完全分离的输入和输出空间的相关数据集中利用规律性，以及（3）在看似无关的任务中利用规律性，胜过胜过任务特定的任务单任务模型和多项任务模型和多项式学习替代方案。结果表明，即使看似无关的任务也可能源自相似的基础过程，这一事实是，旅行观察者模型可以用来做出更好的预测。
This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is completely agnostic about any particular task being solved; it cares only about measurement locations and their values. This perspective leads to a machine learning framework in which seemingly unrelated tasks can be solved by a single model, by embedding their input and output variables into a shared space. An implementation of the framework is developed in which these variable embeddings are learned jointly with internal model parameters. In experiments, the approach is shown to (1) recover intuitive locations of variables in space and time, (2) exploit regularities across related datasets with completely disjoint input and output spaces, and (3) exploit regularities across seemingly unrelated tasks, outperforming task-specific single-task models and multi-task learning alternatives. The results suggest that even seemingly unrelated tasks may originate from similar underlying processes, a fact that the traveling observer model can use to make better predictions.
