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» On Learning Limiting Programs
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TOCS
1998
83views more  TOCS 1998»
15 years 7 months ago
Using Value Prediction to Increase the Power of Speculative Execution Hardware
This paper presents an experimental and analytical study of value prediction and its impact on speculative execution in superscalar microprocessors. Value prediction is a new para...
Freddy Gabbay, Avi Mendelson
OOPSLA
2010
Springer
15 years 5 months ago
Modular logic metaprogramming
In logic metaprogramming, programs are not stored as plain textfiles but rather derived from a deductive database. While the benefits of this approach for metaprogramming are ob...
Karl Klose, Klaus Ostermann
CORR
2010
Springer
124views Education» more  CORR 2010»
15 years 4 months ago
Lattice model refinement of protein structures
To find the best lattice model representation of a given full atom protein structure is a hard computational problem. Several greedy methods have been suggested where results are ...
Martin Mann, Alessandro Dal Palù
ICML
2007
IEEE
16 years 8 months ago
Combining online and offline knowledge in UCT
The UCT algorithm learns a value function online using sample-based search. The TD() algorithm can learn a value function offline for the on-policy distribution. We consider three...
Sylvain Gelly, David Silver
ICML
2008
IEEE
16 years 8 months ago
Random classification noise defeats all convex potential boosters
A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set. This class...
Philip M. Long, Rocco A. Servedio