CoLES: Contrastive Learning for Event Sequences with Self-Supervision
Dmitrii Babaev, Nikita Ovsov, Ivan Kireev, Maria Ivanova, Gleb Gusev, Ivan Nazarov, Alexander Tuzhilin
2022Proceedings of the 2022 International Conference on Management of Data24 citationsDOIOpen Access PDF
Abstract
We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw data in low-dimensional fixed-length vector representations that could be easily applied in various downstream machine learning tasks. In this paper, we propose a new method "CoLES", which adapts contrastive learning, previously used for audio and computer vision domains, to the discrete event sequences domain in a self-supervised setting.
Topics & Concepts
Event (particle physics)Downstream (manufacturing)Computer scienceArtificial intelligenceDomain (mathematical analysis)Raw dataMachine learningSupervised learningNatural language processingMathematicsEngineeringMathematical analysisProgramming languageArtificial neural networkQuantum mechanicsPhysicsOperations managementDomain Adaptation and Few-Shot LearningTopic ModelingMusic and Audio Processing