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Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements. Yet, this analysis is not amenable in the context of graph learning, as graphs usually belong to different…

Machine Learning · Computer Science 2021-09-21 Cédric Vincent-Cuaz , Titouan Vayer , Rémi Flamary , Marco Corneli , Nicolas Courty

We develop a general theory of Cartesian and non-Cartesian polynomials on products of complex spaces $\mathbb{C}^{n_1} \times \cdots \times \mathbb{C}^{n_k}$. We prove that, for any fixed degree $d \ge 2$, a (Zariski) generic polynomial is…

Algebraic Geometry · Mathematics 2026-05-22 Chun-Yen Shen , Tuyen Trung Truong , Wei-Hsuan Yu

This paper investigates Gaussian Markov random field approximations to nonstationary Gaussian fields using graph representations of stochastic partial differential equations. We establish approximation error guarantees building on the…

Methodology · Statistics 2021-04-28 Daniel Sanz-Alonso , Ruiyi Yang

Graphical models use graphs to compactly capture stochastic dependencies amongst a collection of random variables. Inference over graphical models corresponds to finding marginal probability distributions given joint probability…

Machine Learning · Statistics 2013-04-02 Divyanshu Vats , José M. F. Moura

We provide three infinite families of graphs in the Johnson and Grassmann schemes that are not uniquely determined by their spectrum. We do so by constructing graphs that are cospectral but non-isomorphic to these graphs.

Combinatorics · Mathematics 2023-05-29 Aida Abiad , Jozefien D'haeseleer , Willem H. Haemers , Robin Simoens

The paradigm of linear structural equation modeling readily allows one to incorporate causal feedback loops in the model specification. These appear as directed cycles in the common graphical representation of the models. However, the…

Statistics Theory · Mathematics 2025-07-16 Mathias Drton , Marina Garrote-López , Niko Nikov , Elina Robeva , Y. Samuel Wang

We construct the path integral for one-dimensional non-linear sigma models, starting from a given Hamiltonian operator and states in a Hilbert space. By explicit evaluation of the discretized propagators and vertices we find the correct…

High Energy Physics - Theory · Physics 2009-10-28 Jan de Boer , Bas Peeters , Kostas Skenderis , Peter van Nieuwenhuizen

We formulate a noncommutative generalization of the Ricci flow theory in the framework of spectral action approach to noncommutative geometry. Grisha Perelman's functionals are generated as commutative versions of certain spectral…

Mathematical Physics · Physics 2011-06-02 Sergiu I. Vacaru

In a recent article J. Phys. Compl. 4 (2023) 035005, Kawamoto evoked statistical physics methods for the problem of counting graphs with a prescribed degree sequence. This treatment involved truncating a particular Taylor expansion at the…

Statistical Mechanics · Physics 2024-09-06 Oleg Evnin , Weerawit Horinouchi

The construction of Grassmannian codes in some projective space is of highly mathematical nature and requires strong computational power for the resulting searches. In this paper was constructed, using GAP System for Computational Discrete…

Information Theory · Computer Science 2019-03-25 Ismael Gutiérrez García , Ivan Molina Naizir

The $s$-point correlation function of a Gaussian Hermitian random matrix theory, with an external source tuned to generate a multi-critical singularity, provides the intersection numbers of the moduli space for the $p$-th spin curves…

Mathematical Physics · Physics 2015-02-06 E. Brezin , S. Hikami

In an earlier paper the authors proved that limits of convergent graph sequences can be described by various structures, including certain 2-variable real functions called graphons, random graph models satisfying certain consistency…

Combinatorics · Mathematics 2009-02-10 László Lovász , Balázs Szegedy

Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties. Although many different Gaussian process models are readily available when the…

The Feynman path integral is defined over the space $\mathbb{R}^T$ of all possible paths; it has been a powerful tool to develop Quantum Mechanics. The absolute value of Feynman's integrand is not integrable, then Lebesgue integration…

Mathematical Physics · Physics 2020-03-02 Ricardo Gaitan , M. Guadalupe Morales

In this paper, we obtain explicit formulae for the number of 7-cycles and the total number of paths of lengths 6 and 7 those contain a specific vertex $v_{i}$ in a simple graph G, in terms of the adjacency matrix and with the help of…

Combinatorics · Mathematics 2015-03-12 Nazanin Movarraei

The linear Arithmetic Fundamental Lemma (AFL) conjecture compares intersection numbers on Lubin--Tate deformation spaces with derivatives of orbital integrals. It has been introduced for elliptic orbits in arXiv:1803.07553 and…

Algebraic Geometry · Mathematics 2024-03-19 Qirui Li , Andreas Mihatsch

In trying to generalize the classic Sylvester-Gallai theorem and De Bruijn-Erd\H{o}s theorem in plane geometry, lines and closure lines were previously defined for metric spaces and hypergraphs. Both definitions do not obey the geometric…

Metric Geometry · Mathematics 2014-02-25 Xiaomin Chen , Guangda Huzhang , Peihan Miao , Kuan Yang

We present a geometric proof of the averaging theorem for perturbed dynamical systems on a Riemannian manifold, in the case where the flow of the unperturbed vector field is periodic and the $\mathbb{S}^{1}$-action associated to this vector…

Differential Geometry · Mathematics 2015-12-17 Misael Avendaño Camacho , Guillermo Dávila Rascón

For a simple graph $G$, let $n$ and $m$ denote the number of vertices and edges in $G$, respectively. The Erd\H{o}s-Gallai theorem for paths states that in a simple $P_k$-free graph, $m \leq \frac{n(k-1)}{2}$, where $P_k$ denotes a path…

Combinatorics · Mathematics 2025-05-08 Rajat Adak , L. Sunil Chandran

Grassmann angles improve upon similar concepts of angle between subspaces that measure volume contraction in orthogonal projections, working for real or complex subspaces, and being more efficient when dimensions are different. Their…

General Mathematics · Mathematics 2020-10-08 André L. G. Mandolesi
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