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Interpretations of Deep Learning by Forests and Haar Wavelets
[article]
2019
arXiv
pre-print
This paper presents a basic property of region dividing of ReLU (rectified linear unit) deep learning when new layers are successively added, by which two new perspectives of interpreting deep learning are given. The first is related to decision trees and forests; we construct a deep learning structure equivalent to a forest in classification abilities, which means that certain kinds of ReLU deep learning can be considered as forests. The second perspective is that Haar wavelet represented
arXiv:1906.06706v7
fatcat:gkezdt7z4ffzld6ovlmtrvejbm