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143
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ICML
2009
IEEE
16 years 4 months ago
BoltzRank: learning to maximize expected ranking gain
Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to o...
Maksims Volkovs, Richard S. Zemel
126
Voted
HPDC
2010
IEEE
15 years 4 months ago
Cloud computing paradigms for pleasingly parallel biomedical applications
Cloud computing offers new approaches for scientific computing that leverage the major commercial hardware and software investment in this area. Closely coupled applications are s...
Thilina Gunarathne, Tak-Lon Wu, Judy Qiu, Geoffrey...
120
Voted
ICFEM
2004
Springer
15 years 9 months ago
Learning to Verify Safety Properties
We present a novel approach for verifying safety properties of finite state machines communicating over unbounded FIFO channels that is based on applying machine learning techniqu...
Abhay Vardhan, Koushik Sen, Mahesh Viswanathan, Gu...
134
Voted
SAC
2000
ACM
15 years 8 months ago
The Evolution of the DARWIN System
DARWIN is a web-based system for presenting the results of wind-tunnel testing and computational model analyses to aerospace designers. DARWIN captures the data, maintains the inf...
Joan D. Walton, Robert E. Filman, David J. Korsmey...
127
Voted
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
16 years 4 months ago
Query chains: learning to rank from implicit feedback
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
Filip Radlinski, Thorsten Joachims