We consider a random matrix model with both pairwise and non-pairwise contracted indices. The partition function of the matrix model is similar to that appearing in some replicated systems with random tensor couplings, such as the p-spin spherical model for the spin glass. We analyze the model using Feynman diagrammatic expansions, and provide an exhaustive characterization of the graphs that dominate when the dimensions of the pairwise and (or) non-pairwise contracted indices are large. We apply this to investigate the properties of the wave function of a toy model closely related to a tensor model in the Hamilton formalism, which is studied in a quantum gravity context, and obtain a result in favor of the consistency of the quantum probab...
Ordinary tensor models of rank D≥3 are dominated at large N by tree-like graphs, known as melonic tr...
We study the statistical mechanics of random surfaces generated by NxN one-matrix integrals over ant...
International audienceSeveral machine learning problems such as latent variable model learning and c...
We study a matrix model that has $$\phi _a^i\ (a=1,2,\ldots ,N,\ i=1,2,\ldots ,R)$$ as its dynamical...
International audienceThe study of the statistical properties of random matrices of large size has a...
Akemann G, Baik J, Di Francesco P, eds. The Oxford Handbook of Random Matrix Theory. Oxford: Oxford ...
Tensor models, generalization of matrix models, are studied aiming for quantum gravity in dimensions...
Tensor models play an increasingly prominent role in many fields, notably in machine learning. In se...
The large N limit of a one-dimensional infinite chain of random matrices is investigated. It is foun...
Random-matrix models have been intensively studied in the last few years[1]. Although the bulk of th...
Recent advancement in random matrix theory is beneficial to challenging problems in many disciplines...
Recent advancement in random matrix theory is beneficial to challenging problems in many disciplines...
We introduce a statistical system on random networks of trivalent vertices for the purpose of studyi...
Tensor models generalize matrix models and generate colored triangulations of pseudo-manifolds in di...
With a foreword by Freeman Dyson, the handbook brings together leading mathematicians and physicists...
Ordinary tensor models of rank D≥3 are dominated at large N by tree-like graphs, known as melonic tr...
We study the statistical mechanics of random surfaces generated by NxN one-matrix integrals over ant...
International audienceSeveral machine learning problems such as latent variable model learning and c...
We study a matrix model that has $$\phi _a^i\ (a=1,2,\ldots ,N,\ i=1,2,\ldots ,R)$$ as its dynamical...
International audienceThe study of the statistical properties of random matrices of large size has a...
Akemann G, Baik J, Di Francesco P, eds. The Oxford Handbook of Random Matrix Theory. Oxford: Oxford ...
Tensor models, generalization of matrix models, are studied aiming for quantum gravity in dimensions...
Tensor models play an increasingly prominent role in many fields, notably in machine learning. In se...
The large N limit of a one-dimensional infinite chain of random matrices is investigated. It is foun...
Random-matrix models have been intensively studied in the last few years[1]. Although the bulk of th...
Recent advancement in random matrix theory is beneficial to challenging problems in many disciplines...
Recent advancement in random matrix theory is beneficial to challenging problems in many disciplines...
We introduce a statistical system on random networks of trivalent vertices for the purpose of studyi...
Tensor models generalize matrix models and generate colored triangulations of pseudo-manifolds in di...
With a foreword by Freeman Dyson, the handbook brings together leading mathematicians and physicists...
Ordinary tensor models of rank D≥3 are dominated at large N by tree-like graphs, known as melonic tr...
We study the statistical mechanics of random surfaces generated by NxN one-matrix integrals over ant...
International audienceSeveral machine learning problems such as latent variable model learning and c...