Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly-supervised Temporal Path Representation Learning with Contrastive
  Curriculum Learning -- Extended Version   In step with the digitalization of transportation, we are witnessing a
growing range of path-based smart-city applications, e.g., travel-time
estimation and travel path ranking. A temporal path(TP) that includes temporal
information, e.g., departure time, into the path is fundamental to enable such
applications. In this setting, it is essential to learn generic temporal path
representations(TPRs) that consider spatial and temporal correlations
simultaneously and that can be used in different applications, i.e., downstream
tasks. Existing methods fail to achieve the goal since (i) supervised methods
require large amounts of task-specific labels when training and thus fail to
generalize the obtained TPRs to other tasks; (ii) through unsupervised methods
can learn generic representations, they disregard the temporal aspect, leading
to sub-optimal results. To contend with the limitations of existing solutions,
we propose a Weakly-Supervised Contrastive (WSC) learning model. We first
propose a temporal path encoder that encodes both the spatial and temporal
information of a temporal path into a TPR. To train the encoder, we introduce
weak labels that are easy and inexpensive to obtain and are relevant to
different tasks, e.g., temporal labels indicating peak vs. off-peak hours from
departure times. Based on the weak labels, we construct meaningful positive and
negative temporal path samples by considering both spatial and temporal
information, which facilities training the encoder using contrastive learning
by pulling closer to the positive samples' representations while pushing away
the negative samples' representations. To better guide contrastive learning, we
propose a learning strategy based on Curriculum Learning such that the learning
performs from easy to hard training instances. Experiments studies verify the
effectiveness of the proposed method.

18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th