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Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs.

S. Nedelkoski, J. Bogatinovski, A. Acker, J. Cardoso, and O. Kao. ICDM, page 1196-1201. IEEE, (2020)

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Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs.S. Nedelkoski, J. Bogatinovski, A. Acker, J. Cardoso, and O. Kao. ICDM, page 1196-1201. IEEE, (2020)Performance Diagnosis in Cloud Microservices Using Deep Learning.L. Wu, J. Bogatinovski, S. Nedelkoski, J. Tordsson, and O. Kao. ICSOC Workshops, volume 12632 of Lecture Notes in Computer Science, page 85-96. Springer, (2020)Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction.A. Acker, T. Wittkopp, S. Nedelkoski, J. Bogatinovski, and O. Kao. FedCSIS, volume 21 of Annals of Computer Science and Information Systems, page 7-10. (2020)QuLog: data-driven approach for log instruction quality assessment.J. Bogatinovski, S. Nedelkoski, A. Acker, J. Cardoso, and O. Kao. ICPC, page 275-286. ACM, (2022)Self-Supervised Anomaly Detection from Distributed Traces.J. Bogatinovski, S. Nedelkoski, J. Cardoso, and O. Kao. UCC, page 342-347. IEEE, (2020)Multi-source Distributed System Data for AI-Powered Analytics.S. Nedelkoski, J. Bogatinovski, A. Mandapati, S. Becker, J. Cardoso, and O. Kao. ESOCC, volume 12054 of Lecture Notes in Computer Science, page 161-176. Springer, (2020)Anomaly Detection from System Tracing Data Using Multimodal Deep Learning.S. Nedelkoski, J. Cardoso, and O. Kao. CLOUD, page 179-186. IEEE, (2019)Multi-source Anomaly Detection in Distributed IT Systems.J. Bogatinovski, and S. Nedelkoski. ICSOC Workshops, volume 12632 of Lecture Notes in Computer Science, page 201-213. Springer, (2020)Learning More Expressive Joint Distributions in Multimodal Variational Methods.S. Nedelkoski, M. Bogojeski, and O. Kao. LOD (1), volume 12565 of Lecture Notes in Computer Science, page 137-149. Springer, (2020)Deep anomaly detection in distributed software systems.S. Nedelkoski. Technical University of Berlin, Germany, (2021)

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Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs.

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Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs.