Gaussian Sheaf Neural Networks derive a sheaf Laplacian for Gaussian node features on graphs to preserve their geometric structure during message passing.
Eigenvalues, invariant factors, highest weights, and
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Eigenvalues of Haar-random matrices over Z_p are asymptotically evenly distributed among algebraic extensions of Q_p by degree, with all but a bounded expected number lying in the maximal unramified extension Q_p^un; analogous results hold for roots of random Haar polynomials over Z_p.
citing papers explorer
-
Gaussian Sheaf Neural Networks
Gaussian Sheaf Neural Networks derive a sheaf Laplacian for Gaussian node features on graphs to preserve their geometric structure during message passing.
-
Eigenvalue Distribution of $p$-adic Random Matrices Among Algebraic Extensions, with an Analogue for $p$-adic Random Polynomials
Eigenvalues of Haar-random matrices over Z_p are asymptotically evenly distributed among algebraic extensions of Q_p by degree, with all but a bounded expected number lying in the maximal unramified extension Q_p^un; analogous results hold for roots of random Haar polynomials over Z_p.