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SIGIR
2011
ACM
14 years 9 months ago
Learning to rank from a noisy crowd
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Abhimanu Kumar, Matthew Lease
HPCA
2012
IEEE
14 years 1 months ago
Pacman: Tolerating asymmetric data races with unintrusive hardware
Data races are a major contributor to parallel software unreliability. A type of race that is both common and typically harmful is the Asymmetric data race. It occurs when at leas...
Shanxiang Qi, Norimasa Otsuki, Lois Orosa Nogueira...
TOG
2012
184views Communications» more  TOG 2012»
13 years 8 months ago
Point sampling with general noise spectrum
Point samples with different spectral noise properties (often defined using color names such as white, blue, green, and red) are important for many science and engineering discip...
Yahan Zhou, Haibin Huang, Li-Yi Wei, Rui Wang
CONCUR
2012
Springer
13 years 8 months ago
Linearizability with Ownership Transfer
Abstract. Linearizability is a commonly accepted notion of correctness for libraries of concurrent algorithms. Unfortunately, it assumes a complete isolation between a library and ...
Alexey Gotsman, Hongseok Yang
AAAI
2012
13 years 8 months ago
Supervised Probabilistic Robust Embedding with Sparse Noise
Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise re...
Yu Zhang, Dit-Yan Yeung, Eric P. Xing
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