LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak
Supervision

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LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision

2 November 2021

Thorsten Wittkopp

Philipp Wiesner

Dominik Scheinert

Alexander Acker

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Papers citing "LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision"

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Title
A2Log: Attentive Augmented Log Anomaly Detection Thorsten Wittkopp Alexander Acker S. Nedelkoski Jasmin Bogatinovski Dominik Scheinert Wu Fan O. Kao 56 22 0 20 Sep 2021
Decentralized Federated Learning Preserves Model and Data Privacy Thorsten Wittkopp Alexander Acker 51 20 0 01 Feb 2021
Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs S. Nedelkoski Jasmin Bogatinovski Alexander Acker Jorge Cardoso O. Kao 64 129 0 21 Aug 2020
Text Classification Algorithms: A Survey Kamran Kowsari K. Meimandi Mojtaba Heidarysafa Sanjana Mendu Laura E. Barnes Donald E. Brown 74 1,299 0 17 Apr 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova VLM SSL SSeg 1.8K 95,114 0 11 Oct 2018
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 722 132,199 0 12 Jun 2017
Data Programming: Creating Large Training Sets, Quickly Alexander Ratner Christopher De Sa Sen Wu Daniel Selsam Christopher Ré 197 718 0 25 May 2016
A bagging SVM to learn from positive and unlabeled examples F. Mordelet Jean-Philippe Vert 105 289 0 05 Oct 2010