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JMLR
2006
169views more  JMLR 2006»
13 years 7 months ago
Bayesian Network Learning with Parameter Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
SIGMETRICS
2008
ACM
13 years 6 months ago
Scheduling despite inexact job-size information
Motivated by the optimality of Shortest Remaining Processing Time (SRPT) for mean response time, in recent years many computer systems have used the heuristic of "favoring sm...
Adam Wierman, Misja Nuyens
IOR
2007
106views more  IOR 2007»
13 years 6 months ago
Planning and Scheduling by Logic-Based Benders Decomposition
We combine mixed integer linear programming (MILP) and constraint programming (CP) to solve an important class of planning and scheduling problems. Tasks are allocated to faciliti...
John N. Hooker
BC
2005
101views more  BC 2005»
13 years 6 months ago
A control theory approach to the analysis and synthesis of the experimentally observed motion primitives
Recent experiments on frogs and rats, have led to the hypothesis that sensory-motor systems are organized into a finite number of linearly combinable modules; each module generates...
Francesco Nori, Ruggero Frezza
TIT
2008
140views more  TIT 2008»
13 years 6 months ago
A Resource Framework for Quantum Shannon Theory
Quantum Shannon theory is loosely defined as a collection of coding theorems, such as classical and quantum source compression, noisy channel coding theorems, entanglement distilla...
Igor Devetak, Aram Wettroth Harrow, Andreas J. Win...