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Unsupervised Path Representation Learning with Curriculum Negative Sampling
[article]
2021
arXiv
pre-print
Path representations are critical in a variety of transportation applications, such as estimating path ranking in path recommendation systems and estimating path travel time in navigation systems. Existing studies often learn task-specific path representations in a supervised manner, which require a large amount of labeled training data and generalize poorly to other tasks. We propose an unsupervised learning framework Path InfoMax (PIM) to learn generic path representations that work for
arXiv:2106.09373v1
fatcat:j5kxg7kybzgo5npx2pinrpjofe