Feb. 15, 2024, 5:43 a.m. | Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato

cs.LG updates on arXiv.org arxiv.org

arXiv:2305.18484v2 Announce Type: replace-cross
Abstract: Symmetry learning has proven to be an effective approach for extracting the hidden structure of data, with the concept of equivariance relation playing the central role. However, most of the current studies are built on architectural theory and corresponding assumptions on the form of data. We propose Neural Fourier Transform (NFT), a general framework of learning the latent linear action of the group without assuming explicit knowledge of how the group acts on data. We …

abstract arxiv assumptions concept cs.lg current data form fourier general hidden playing representation representation learning role stat.ml studies symmetry theory type

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