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Recently machine learning algorithms based on deep layered artificial neuralnetworks (DNNs) have been applied to a wide variety of high energy physicsproblems such as jet tagging or event classification. We explore a simple buteffective preprocessing step which transforms each real-valued observationalquantity or input feature into a binary number with a fixed number of digits.Each binary digit represents the quantity or magnitude in different scales. Wehave shown that this approach improves the performance of DNNs significantlyfor some specific tasks without any further complication in featureengineering. We apply this multi-scale distributed binary representation todeep learning on b-jet tagging using daughter particles' momenta and vertexinformation.

Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging