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ICML
2004
IEEE
16 years 5 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
ICML
2000
IEEE
16 years 5 months ago
Combining Reinforcement Learning with a Local Control Algorithm
We explore combining reinforcement learning with a hand-crafted local controller in a manner suggested by the chaotic control algorithm of Vincent, Schmitt and Vincent (1994). A c...
Andrew G. Barto, Jette Randløv, Michael T. ...
HASE
2008
IEEE
15 years 10 months ago
Small Logs for Transactional Services: Distinction is Much More Accurate than (Positive) Discrimination
For complex services, logging is an integral part of many middleware aspects, especially, transactions and monitoring. In the event of a failure, the log allows us to deduce the c...
Debmalya Biswas, Thomas Gazagnaire, Blaise Genest
135
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WDAG
2005
Springer
90views Algorithms» more  WDAG 2005»
15 years 9 months ago
Proving Atomicity: An Assertional Approach
Atomicity (or linearizability) is a commonly used consistency criterion for distributed services and objects. Although atomic object implementations are abundant, proving that algo...
Gregory Chockler, Nancy A. Lynch, Sayan Mitra, Jos...
ICML
1996
IEEE
15 years 8 months ago
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning
This paper presents a direct reinforcement learning algorithm, called Finite-Element Reinforcement Learning, in the continuous case, i.e. continuous state-space and time. The eval...
Rémi Munos