Author

Alipanah Parviz

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Preprint Jul 2026

Reassessing Muon for Matrix Factorization

It is found that Muon does not consistently outperform AdamW in this setting and that several previously reported advantages are sensitive to hyperparameter choices, providing a more nuanced picture of when spectrum-aware orthogonalization is beneficial and arguing for evaluating modern optimizers on controlled problems in addition to end-to-end benchmarks.

Alipanah Parviz, Gal Mishne, Alex Cloninger · 0 citations

#graph neural networks Preprint Aug 2026

Nonlinear Laplacians Improve Signed-Directed Graph Learning

This work introduces a non-linear Laplacian operator specific to signed and directed networks (NLSD) and proposes an efficient spectral GNN framework (NLSD-GNN), which not only integrates signed and directional data effectively but also achieves superior performance across diverse datasets.

Alipanah Parviz, Yuichi Yoshida · 3 citations