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JMLR
2012
13 years 6 months ago
Structured Output Learning with High Order Loss Functions
Often when modeling structured domains, it is desirable to leverage information that is not naturally expressed as simply a label. Examples include knowledge about the evaluation ...
Daniel Tarlow, Richard S. Zemel
FPL
2009
Springer
79views Hardware» more  FPL 2009»
15 years 8 months ago
A reconfigurable architecture for the Phylogenetic Likelihood Function
As FPGA devices become larger, more coarse-grain modules coupled with large scale reconfigurable fabric become available, thus enabling new classes of applications to run effici...
Nikolaos Alachiotis, Alexandros Stamatakis, Euripi...
ICLP
2004
Springer
15 years 9 months ago
Applying CLP to Predict Extra-Functional Properties of Component-Based Models
A component is the basic re-usable unit of composition to build composite systems by connecting to others through their provided and required ports. Checking the functional complia...
Olivier Defour, Jean-Marc Jézéquel, ...
CCE
2007
15 years 3 months ago
Adaptive optimisation of noisy black-box functions inherent in microscopic models
For systems where exact constitutive relations are unknown, a microscopic level description can be alternatively used. As microscopic simulations are computationally expensive, th...
Eddie Davis, Marianthi G. Ierapetritou
SIGECOM
2009
ACM
114views ECommerce» more  SIGECOM 2009»
15 years 10 months ago
Policy teaching through reward function learning
Policy teaching considers a Markov Decision Process setting in which an interested party aims to influence an agent’s decisions by providing limited incentives. In this paper, ...
Haoqi Zhang, David C. Parkes, Yiling Chen