Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Traveling Observer Model: Multi-task Learning Through Spatial
  Variable Embeddings   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.

24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph