most citedTime-Series Anomaly Detection Service at Microsoft

588 citations · 627 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL2020

AutoADR: Automatic Model Design for Ad Relevance

Yiren Chen, Yaming Yang, Hong Sun +7

Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, th…

cs.LG202030 cited

Multivariate Time-series Anomaly Detection via Graph Attention Network

Hang Zhao, Yujing Wang, Juanyong Duan +7

Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress…

cs.LG20205 cited

Automated Model Selection for Time-Series Anomaly Detection

Yuanxiang Ying, Juanyong Duan, Chunlei Wang +3

Time-series anomaly detection is a popular topic in both academia and industrial fields. Many companies need to monitor thousands of temporal signals for their applications and ser…

cs.CV2020

Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering

Zihao Zhu, Jing Yu, Yujing Wang +3

Fact-based Visual Question Answering (FVQA) requires external knowledge beyond visible content to answer questions about an image, which is challenging but indispensable to achieve…

cs.LG20194 cited

TextNAS: A Neural Architecture Search Space tailored for Text Representation

Yujing Wang, Yaming Yang, Yiren Chen +7

Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how…

cs.LG2019588 cited

Time-Series Anomaly Detection Service at Microsoft

Hansheng Ren, Bixiong Xu, Yujing Wang +7

Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomal…