This work studies neural architectures for classifying symmetric positive-definite matrices, focusing on congruence-like layers, in which the input matrix is multiplied on the left and right by a (possibly rectangular) weight matrix $W$ and its transpose. Such layers lie at the core of the celebrated SPDNet and have also been employed independently for dimensionality reduction on positive-definite data.
arXiv:2406. 08966v3 Announce Type: replace Abstract: The separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy for its expressivity.
By Marco Pacini, Xiaowen Dong, Bruno Lepri, Gabriele Santin
arXiv:2607. 18148v1 Announce Type: new Abstract: We investigate the extent to which totally positive matrices can be distinguished through the highest-order coefficients of their characteristic polynomials.
By Tiago Closs, Leandro Farina
arXiv:2602. 22895v2 Announce Type: replace-cross Abstract: Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages.
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By Anuragine S A, Prem Jagadeesan