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Yichen Zhu
Researcher at University of Toronto
Publications - 14
Citations - 297
Yichen Zhu is an academic researcher from University of Toronto. The author has contributed to research in topics: Computer science & Automatic summarization. The author has an hindex of 2, co-authored 6 publications receiving 91 citations.
Papers
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Proceedings ArticleDOI
LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs
Weibin Meng,Ying Liu,Yichen Zhu,Shenglin Zhang,Dan Pei,Yuqing Liu,Yihao Chen,Ruizhi Zhang,Shimin Tao,Pei Sun,Rong Zhou +10 more
TL;DR: Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which has not been done by any previous work.
Proceedings ArticleDOI
LogParse: Making Log Parsing Adaptive through Word Classification
Weibin Meng,Ying Liu,Federico Zaiter,Shenglin Zhang,Yihao Chen,Yuzhe Zhang,Yichen Zhu,En Wang,Ruizhi Zhang,Shimin Tao,Dian Yang,Rong Zhou,Dan Pei +12 more
TL;DR: This work proposes LogParse, an adaptive log parsing framework, to support intra-service and cross-service incremental template learning and update, which turns the template generation problem into a word classification problem and learns the features of template words and variable words.
Proceedings Article
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher
Yichen Zhu,Yi Wang +1 more
Posted Content
Summarizing Unstructured Logs in Online Services.
Weibin Meng,Federico Zaiter,Yuheng Huang,Ying Liu,Shenglin Zhang,Yuzhe Zhang,Yichen Zhu,Tianke Zhang,En Wang,Zuomin Ren,Feng Wang,Shimin Tao,Dan Pei +12 more
TL;DR: This work proposes LogSummary, an automatic, unsupervised end-to-end log summarization framework for online services that obtains the summarized triples of important logs for a given log sequence with a new triple ranking approach using the global knowledge learned from all logs.
Journal ArticleDOI
LogStamp: Automatic Online Log Parsing Based on Sequence Labelling
Shimin Tao,Weibin Meng,Yimeng Chen,Yichen Zhu,Ying Liu,Chunning Du,Tao Han,Yongpeng Zhao,Xiangguang Wang,Hao Yang +9 more
TL;DR: An automatic online log parsing method, name as LogStamp, which can achieve high accuracy with only a small portion of the training set and can achieve an average accuracy of 0.956 when using only 10% of the data training.