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AIPS
1994
13 years 9 months ago
A Planner with Quality Goal and Its Speed-up Learning for Optimization Problem
Aimsof traditional planners had beenlimited to finding a sequenceof operators rather than finding an optimal or neax-optimalfinal state. Consequent]y, the performanceimprovementsy...
Masahiko Iwamoto
APLAS
2010
ACM
13 years 7 months ago
Automatically Inferring Quantified Loop Invariants by Algorithmic Learning from Simple Templates
Abstract. By combining algorithmic learning, decision procedures, predicate abstraction, and simple templates, we present an automated technique for finding quantified loop invaria...
Soonho Kong, Yungbum Jung, Cristina David, Bow-Yaw...
PVLDB
2010
110views more  PVLDB 2010»
13 years 6 months ago
Evaluation of entity resolution approaches on real-world match problems
Despite the huge amount of recent research efforts on entity resolution (matching) there has not yet been a comparative evaluation on the relative effectiveness and efficiency of ...
Hanna Köpcke, Andreas Thor, Erhard Rahm
JMLR
2010
139views more  JMLR 2010»
13 years 2 months ago
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
Alternating Gibbs sampling is the most common scheme used for sampling from Restricted Boltzmann Machines (RBM), a crucial component in deep architectures such as Deep Belief Netw...
Guillaume Desjardins, Aaron C. Courville, Yoshua B...
AAAI
2000
13 years 9 months ago
Information Extraction with HMM Structures Learned by Stochastic Optimization
Recent research has demonstrated the strong performance of hidden Markov models applied to information extraction--the task of populating database slots with corresponding phrases...
Dayne Freitag, Andrew McCallum