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» Sampling Techniques for Large, Dynamic Graphs
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83
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ICCAD
2007
IEEE
161views Hardware» more  ICCAD 2007»
15 years 9 months ago
Clustering based pruning for statistical criticality computation under process variations
— We present a new linear time technique to compute criticality information in a timing graph by dividing it into “zones”. Errors in using tightness probabilities for critica...
Hushrav Mogal, Haifeng Qian, Sachin S. Sapatnekar,...
119
Voted
NAACL
2010
14 years 10 months ago
Hitting the Right Paraphrases in Good Time
We present a random-walk-based approach to learning paraphrases from bilingual parallel corpora. The corpora are represented as a graph in which a node corresponds to a phrase, an...
Stanley Kok, Chris Brockett
CVPR
2011
IEEE
14 years 10 months ago
Dynamic Batch Mode Active Learning
Active learning techniques have gained popularity in reducing human effort to annotate data instances for inducing a classifier. When faced with large quantities of unlabeled dat...
Shayok Chakraborty, Vineeth Balasubramanian, Sethu...
111
Voted
NDSS
2009
IEEE
15 years 7 months ago
Scalable, Behavior-Based Malware Clustering
Anti-malware companies receive thousands of malware samples every day. To process this large quantity, a number of automated analysis tools were developed. These tools execute a m...
Ulrich Bayer, Paolo Milani Comparetti, Clemens Hla...
MICRO
2009
IEEE
326views Hardware» more  MICRO 2009»
15 years 7 months ago
DDT: design and evaluation of a dynamic program analysis for optimizing data structure usage
Data structures define how values being computed are stored and accessed within programs. By recognizing what data structures are being used in an application, tools can make app...
Changhee Jung, Nathan Clark