Searched for: subject:"Approximation%5C+algorithms"
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Munk, J. (author), Kober, J. (author), Babuska, R. (author)
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The drawback of this end-to-end learning is that it...
conference paper 2016
document
Byrka, Jaroslaw (author), Li, S. (author), Rybicki, Bartosz (author)
times OPT, where α journal article 2014
document
Feldman, A.B. (author)
Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation algorithms for three problems closely related to...
doctoral thesis 2010
document
Byrka, J. (author), Aardal, K.I. (author)
We obtain a 1.5-approximation algorithm for the metric uncapacitated facility location (UFL) problem, which improves on the previously best known 1.52-approximation algorithm by Mahdian, Ye, and Zhang. Note that the approximability lower bound by Guha and Khuller is 1.463 . . . . An algorithm is a (?f ,?c)-approximation algorithm if the solution...
journal article 2010
Searched for: subject:"Approximation%5C+algorithms"
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