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CP-CHARM: Segmentation-Free Image Classification Made Accessible

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Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

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Measurement of the τ lepton polarization in Z boson decays in proton-proton collisions at √s=13 TeV

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Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

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Utilization of small molecules as passivation materials for perovskite solar cells (PSCs) has gained significant attention recently, with hundreds of small molecules demonstrating passivation effects. In this study, a high-accuracy machine learning model i ...

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Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

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Random matrix methods for high-dimensional machine learning models

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In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

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The polarization of tau leptons is measured using leptonic and hadronic tau lepton decays in Z -> tau(+)tau(-) events in proton-proton collisions at root s = 13 TeV recorded by CMS at the CERN LHC with an integrated luminosity of 36.3 fb(-1). The measured ...

Springer Nature2024