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Modified Grid Searches for Hyper-Parameter Optimization
2020Black-box optimization aims to find the optimum of an unknown function only by evaluating it over different points in the space. An important application of black-box optimization in Machine Learning is the computationally expensive tuning of the hyper-parameters, which requires to try different configurations and measure the validation error over each
David López +2 more
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Range Searching in Categorical Data: Colored Range Searching on Grid
2002Range searching, a fundamental problem in numerous applications areas, has been widely studied in computational geometry and spatial databases. Given a set of geometric objects, a typical range query asks for reporting all the objects that intersect a query object. However in many applications, including databases and network routing, input objects are
Pankaj K. Agarwal +2 more
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Conditionally-uniform Feasible Grid Search Algorithm
2012We present and evaluate a numerical optimization method (together with an algorithm for choosing the starting values) pertinent to the constrained optimization problem arising in the estimation of the GARCH models with inequality constraints, in particular the Simplied Component GARCH Model (SCGARCH), together with algorithms for the objective function
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Smart Grid — The New and Improved Power Grid: A Survey
IEEE Communications Surveys and Tutorials, 2012Satyajayant Misra +2 more
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A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges
IEEE Communications Surveys and Tutorials, 2013Yi Qian
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Electrical Energy Storage for the Grid: A Battery of Choices
Science, 2011Bruce S Dunn, Jean Marie Tarascon
exaly
Combining Power and Communication Network Simulation for Cost-Effective Smart Grid Analysis
IEEE Communications Surveys and Tutorials, 2014Chris Develder
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