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• W3162140458 abstract "We propose a probe for the analysis of deep learning architectures that is based on machine learning and approximation theoretical principles. Given a deep learning architecture and a training set, during or after training, the Sparsity Probe allows to analyze the performance of intermediate layers by quantifying the geometrical features of representations of the training set. We show how the Sparsity Probe enables measuring the contribution of adding depth to a given architecture, to detect under-performing layers, etc., all this without any auxiliary test data set." @default.
• W3162140458 created "2021-05-24" @default.
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• W3162140458 date "2021-05-14" @default.
• W3162140458 modified "2023-09-27" @default.
• W3162140458 title "Sparsity-Probe: Analysis tool for Deep Learning Models" @default.
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• W3162140458 doi "https://doi.org/10.48550/arxiv.2105.06849" @default.
• W3162140458 hasPublicationYear "2021" @default.
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