A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The abstract theorem is extended to higher genus surfaces.
Partition functions for dimers on closed oriented surfaces are known to be alternating sums of Pfaffians of Kasteleyn matrices. In this paper, we obtain the formula for the coefficients in terms of discrete spin structures.
The Kac-Ward formula allows to compute the Ising partition function on a planar graph G with straight edges from the determinant of a matrix of size 2N, where N denotes the number of edges of G. In this paper, we extend this formula to any finite graph: the partition function can be written as an alternating sum of the…
The conformal invariance and universality results of Chelkak-Smirnov on the two-dimensional Ising model hold for isoradial planar graphs with critical weights. Motivated by the problem of extending these results to a wider class of graphs, we define a generalized notion of s-holomorphicity for functions on arbitrary we…
Graphs and local systems count multiwebs.
This paper develops upper and lower bounds on the influence measure in a network, more precisely, the expected number of nodes that a seed set can influence in the independent cascade model. In particular, our bounds exploit nonbacktracking walks, Fortuin-Kasteleyn-Ginibre (FKG) type inequalities, and are computed by m…
Fox coloring provides a combinatorial framework for studying dihedral representations of the knot group. The less well-known concept of Dehn coloring captures the same data. Recent work of Carter-Silver-Williams clarifies the relationship between the two focusing on how one transitions between Fox and Dehn colorings. I…
Machine learning classifies phases of spin models using improved correlation configurations.
Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of …
We simplify matrix computations for block matrices, especially useful for covariance and correlation matrices.
Computes isotropy subgroups of orthogonal matrices acting on Hermitian matrices.
Study on random matrices in deep neural networks with IID entries.
Financial markets analyzed by reducing correlation matrix complexity.
Study isotropy groups for complex orthogonal and skew-symmetric matrices.
The paper deals with distribution of singular values of product of random matrices arising in the analysis of deep neural networks. The matrices resemble the product analogs of the sample covariance matrices, however, an important difference is that the population covariance matrices, which are assumed to be non-random…
We introduce Clique Matrices as an alternative representation of undirected graphs, being a generalisation of the incidence matrix representation. Here we use clique matrices to decompose a graph into a set of possibly overlapping clusters, de ned as well-connected subsets of vertices. The decomposition is based on a s…
Study of strictly accretive matrices using Finsler geometry.
We prove that semialgebraic sets of rectangular matrices of a fixed rank, of skew-symmetric matrices of a fixed rank and of real symmetric matrices whose eigenvalues have prescribed multiplicities are minimal submanifolds of the space of real matrices of a given size.
Minimal spectral radii found for specific matrix types.
Researchers develop geodesics for a new metric on correlation matrices.
We find an upper bound for geodesic distances associated to monotone Riemannian metrics on positive definite matrices and density matrices.
New methods for sketching non-PSD matrices improve regression and optimization tasks.
Improved method for computing Fréchet means on SPD matrices.
Algorithm finds isotropy subgroups of orthogonal similarity on symmetric matrices.
We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the estimation of the joint eigenstructure of a set of matrices, with applications in signal processing, linear algebra, and tensor decomposition.…
Method estimates M-matrices in graphical models with improved accuracy.
Algorithm calculates Seifert matrices for colored links.
In this paper we extend DDVV-type inequalities involving the Frobenius norm of commutators from real symmetric and skew-symmetric matrices to Hermitian and skew-Hermitian matrices.
Proposes a new Sliced-Wasserstein distance for covariance matrices in M/EEG signals.
Study extends bounds on sample covariance matrices with general dependence.
New -means method clusters radar image sequences using SPD matrices.
Kaleidoscope matrices improve model quality and inference speed.
Monge matrices and their permuted versions known as pre-Monge matrices naturally appear in many domains across science and engineering. While the rich structural properties of such matrices have long been leveraged for algorithmic purposes, little is known about their impact on statistical estimation. In this work, we …
A method learns matrix factorization from diverse matrices and applies the knowledge to unseen matrices.
The paper identifies and critiques problems with risk matrices using ordinal scales.
Complex systems are typically represented by large ensembles of observations. Correlation matrices provide an efficient formal framework to extract information from such multivariate ensembles and identify in a quantifiable way patterns of activity that are reproducible with statistically significant frequency compared…
Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computation…
In this paper we generalize the known DDVV-type inequalities for real (skew-)symmetric and complex (skew-)Hermitian matrices to arbitrary real, complex and quaternionic matrices. Inspired by the Erdős-Mordell inequality, we establish the DDVV-type inequalities for matrices in the subspaces spanned by a Clifford system …
In this paper, we study the problem of compressed sensing using binary measurement matrices and -norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices. We establish sufficient conditi…
Study of J-Hermitian matrices and geometric mean definition.
Polynomial time algorithm matches correlated Gaussian matrices without vanishing correlation.
We study the differential geometric properties of the manifold of non-singular symmetric real matrices endowed with the trace metric; in case of positive definite matrices we describe the full group of isometries
This work compresses heavy-tailed weight matrices for tighter generalization bounds.
Simple bounds for covariance and Gram matrices across various settings.
Paper develops new method for detecting latent structure in large symmetric data matrices.
The article explains how to estimate confusion matrices for classifiers using unlabeled data.
New metrics defined for full-rank correlation matrices, ensuring unique operations.
New metric tensor field on symmetric matrices simplifies eigenvector computation.