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NIPS
1997
15 years 5 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung
117
Voted
IPPS
1997
IEEE
15 years 8 months ago
Time-Stamping Algorithms for Parallelization of Loops at Run-Time
In this paper, we present two new run-time algorithms for the parallelization of loops that have indirect access patterns. The algorithms can handle any type of loop-carried depen...
Cheng-Zhong Xu, Vipin Chaudhary
SSS
2009
Springer
138views Control Systems» more  SSS 2009»
15 years 10 months ago
Analysis of Wireless Sensor Network Protocols in Dynamic Scenarios
We describe an approach to the analysis of protocols for wireless sensor networks in scenarios with mobile nodes and dynamic link quality. The approach is based on the theorem prov...
Cinzia Bernardeschi, Paolo Masci, Holger Pfeifer
TACAS
2000
Springer
121views Algorithms» more  TACAS 2000»
15 years 7 months ago
Using Static Analysis to Improve Automatic Test Generation
Conformance testing is still the main industrial validation technique for telecommunication protocols. The automatic construction of test cases based on the model approach is hinde...
Marius Bozga, Jean-Claude Fernandez, Lucian Ghirvu
124
Voted
ICML
2010
IEEE
15 years 5 months ago
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
We introduce new, efficient algorithms for value iteration with multiple reward functions and continuous state. We also give an algorithm for finding the set of all nondominated a...
Daniel J. Lizotte, Michael H. Bowling, Susan A. Mu...