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Last year, MIT researchers announced that they had built “liquid” neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job an...
Functional equations for transfer-matrix operators in open Hecke chain models
Functional equations transfer-matrix operators open Hecke chain models
2010/4/6
We consider integrable open chain models formulated in terms of generators of affine Hecke algebras. The hierarchy of commutative elements (which are analogs of the commutative transfer-matrices) are ...
Poisson Structures of Calogero-Moser and Ruijsenaars-Schneider Models
Ruijsenaars-Schneider Models Calogero-Moser
2010/4/7
We examine the Hamiltonian structures of some Calogero-Moser and Ruijsenaars-Schneider N-body integrable models. We propose explicit formulations of the bihamiltonian structures for the discrete model...
Asymptotic Properties of the Maximum Likelihood Estimate in Generalized Linear Models with Stochastic Regressors
Generalized linear models Consistency Asymptotic normality
2007/12/12
For generalized linear models (GLM), in case the regressors are stochastic and have different distributions, the asymptotic properties of the maximum likelihood estimate (MLE) $\hat{\beta}_n$ of the p...