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.

该论文提出了一种基于机器学习框架的数据预测方案，能够将看似无关的任务通过将其输入和输出变量嵌入到共享空间中解决，并在实验中显示了远优于单任务和多任务学习替代方案的表现。