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.
翻译:本文将一般预测系统设置为环绕连续空间的观察者, 测量某些地点的数值, 并预测其他地点的数值。 观察者对正在解决的任何特定任务完全不知情; 它只关心测量地点及其价值。 这个视角导致一个机器学习框架, 通过将输入和输出变量嵌入一个共享空间, 可以在其中解决似乎无关的任务。 正在开发一个框架, 使这些变量嵌入与内部模型参数共同学习。 在实验中, 方法显示:(1) 恢复空间和时间变量的直观位置, (2) 利用完全脱节投入和输出空间的相关数据集的常规性, (3) 利用看起来无关的任务的常规性, 超过任务特有的单项任务模型和多任务学习的替代方法。 结果显示, 即使是看似不相干的任务也可能来自类似的基本过程, 旅行观察者模型可以用来做更好的预测。