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Topology and geometry of data manifold in deep learning
[article]
2022
arXiv
pre-print
Despite significant advances in the field of deep learning in applications to various fields, explaining the inner processes of deep learning models remains an important and open question. The purpose of this article is to describe and substantiate the geometric and topological view of the learning process of neural networks. Our attention is focused on the internal representation of neural networks and on the dynamics of changes in the topology and geometry of the data manifold on different
arXiv:2204.08624v1
fatcat:silqzmxqmzchjpuwliuiq3vqty