L
Ling Huang
Researcher at Intel
Publications - 78
Citations - 8942
Ling Huang is an academic researcher from Intel. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 33, co-authored 60 publications receiving 8157 citations. Previous affiliations of Ling Huang include University of California, Berkeley.
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Journal ArticleDOI
Tapestry: a resilient global-scale overlay for service deployment
TL;DR: Experimental results show that Tapestry exhibits stable behavior and performance as an overlay, despite the instability of the underlying network layers, illustrating its utility as a deployment infrastructure.
Proceedings ArticleDOI
Adversarial machine learning
TL;DR: In this article, the authors discuss an emerging field of study: adversarial machine learning (AML), the study of effective machine learning techniques against an adversarial opponent, and give a taxonomy for classifying attacks against online machine learning algorithms.
Proceedings ArticleDOI
Detecting large-scale system problems by mining console logs
TL;DR: In this article, a general methodology to mine this rich source of information to automatically detect system runtime problems was proposed, combining source code analysis with information retrieval to create composite features and then analyze these features using machine learning to detect operational problems.
Proceedings Article
Detecting Large-Scale System Problems by Mining Console Logs
TL;DR: This work first parse console logs by combining source code analysis with information retrieval to create composite features, and then analyzes these features using machine learning to detect operational problems to automatically detect system runtime problems.
Proceedings ArticleDOI
Fast approximate spectral clustering
TL;DR: This work develops a general framework for fast approximate spectral clustering in which a distortion-minimizing local transformation is first applied to the data, and develops two concrete instances of this framework, one based on local k-means clustering (KASP) and onebased on random projection trees (RASP).