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Learning computationally expensive functions
Analysing data from an experiment at the Large Hadron Collider at CERN requires large amounts of computing power. In addition to large amounts of experimental data, each individual analysis requires large amounts of simulated data. This production of simulated data is the single largest consu...
By Tim Head
Applying Machine Learning to Detect Code Quality Issues
Detecting the potential problems in the code before the product is released can prevent the problems in production and lower the cost of the system operation. The automated code review tools are relying on detecting code patterns that are know to cause problems. This methods are unable to find n...
When to worry about negative weights in MVA training for HEP analysis
A discussion at DS@LHC2015 concerned the treatment of negative weights in MVA training. This is relevant for HEP since the advent of MC generators at NLO in QCD, as [the matching of Matrix-Element to Parton-Shower generators](http://dx.doi.org/10.1088/1126-6708/2002/06/029) assigns negative weig...
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