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Inferring Convolutional Neural Networks' accuracies from their architectural characterizations
Convolutional Neural Networks (CNNs) have shown strong promise for analy...
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DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
In astronomy, neural networks are often trained on simulation data with ...
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A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
We have developed a convolutional neural network (CNN) that can make a p...
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CMS Analysis and Data Reduction with Apache Spark
Experimental Particle Physics has been at the forefront of analyzing the...
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The archive solution for distributed workflow management agents of the CMS experiment at LHC
The CMS experiment at the CERN LHC developed the Workflow Management Arc...
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Using Big Data Technologies for HEP Analysis
The HEP community is approaching an era were the excellent performances ...
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A Dynamic Reduction Network for Point Clouds
Classifying whole images is a classic problem in machine learning, and g...
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Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms
We present a comparison of methods for uncertainty quantification (UQ) i...
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Bayesian model averaging for analysis of lattice field theory results
Statistical modeling is a key component in the extraction of physical re...
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Domain adaptation techniques for improved cross-domain study of galaxy mergers
In astronomy, neural networks are often trained on simulated data with t...
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Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
Tracking is one of the most time consuming aspects of event reconstructi...
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