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2010 – 2019
- 2019
[j3]Anil Kumar Nalini Chandran, Sunok Moon, Yo-Han Yoo, Yoon-Shil Gho, Peijian Cao, Rita Sharma
, Manoj K. Sharma, Pamela C. Ronald, Kihong Jung:
A web-based tool for the prediction of rice transcription factor function. Database J. Biol. Databases Curation 2019: baz061 (2019)- 2013
- [i2]Rita Sharma, David Poole:
Symmetric Collaborative Filtering Using the Noisy Sensor Model. CoRR abs/1301.2309 (2013) - 2012
- [i1]Rita Sharma, David Poole:
Efficient Inference in Large Discrete Domains. CoRR abs/1212.2518 (2012) - 2010
- [j2]Rita Sharma, David Poole, Clinton Smyth:
A framework for ontologically-grounded probabilistic matching. Int. J. Approx. Reason. 51(2): 240-262 (2010)
2000 – 2009
- 2009
- [j1]David Poole, Clinton Smyth, Rita Sharma:
Ontology Design for Scientific Theories That Make Probabilistic Predictions. IEEE Intell. Syst. 24(1): 27-36 (2009) - 2008
- [c9]David Poole, Clinton Smyth, Rita Sharma:
Semantic Science and Machine-Accessible Scientific Theories. AAAI Spring Symposium: Semantic Scientific Knowledge Integration 2008: 81-86 - [c8]David Poole, Clinton Smyth, Rita Sharma:
Semantic Science: Ontologies, Data and Probabilistic Theories. URSW (LNCS Vol.) 2008: 26-40 - 2007
- [c7]Rita Sharma, David Poole, Clinton Smyth:
A System for Ontologically-Grounded Probabilistic Matching. BMA 2007 - 2005
- [c6]Rita Sharma, David Poole:
Probability and Equality: A Probabilistic Model of Identity Uncertainty. Canadian AI 2005: 227-231 - [c5]Rita Sharma, David Poole:
Probabilistic Reasoning with Hierarchically Structured Variables. IJCAI 2005: 1391-1397 - 2004
- [c4]Giuseppe Carenini, Rita Sharma:
Exploring More Realistic Evaluation Measures for Collaborative Filtering. AAAI 2004: 749-754 - 2003
- [c3]Rita Sharma, David Poole:
Efficient Inference in Large Discrete Domains. UAI 2003: 535-542 - 2001
- [c2]Tim Braun, Anne Condon, Alan J. Hu, Kai S. Juse, Marius Laza, Michael Leslie, Rita Sharma:
Proving sequential consistency by model checking. HLDVT 2001: 103-108 - [c1]Rita Sharma, David Poole:
Symmetric Collaborative Filtering Using the Noisy Sensor Model. UAI 2001: 488-495
Coauthor Index
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