Add to ORKGPre-PrintThe process of machine learning can be considered in two stages model selection and parameter estimation. In this paper a technique is presented for constructing dynamical systems with desired qualitative properties. The approach is based on the fact that an n-dimensional nonlinear dynamical system can be decomposed into one gradient and (n-1) Hamiltonian systems. Thus, the model selection stage consists of choosing the gradient and Hamiltonian portions appropriately so that a certain behavior is obtainable. To estimate the parameters, a stably convergent learning rule is presented. This algorithm has been proven to converge to the desired system trajectory for all initial conditions and system inputs. This technique can be u...
Recently, there has been an increasing interest in modelling and computation of physical systems wit...
This paper deals with studying the asymptotical properties of multilayer neural networks models used...
We establish conditions under which Oja-Adams ’ learning models are gradient, semi-gradient, or grad...
The process of machine learning can be considered in two stages model selection and parameter estim...
The process of machine learning can be considered in two stages: model selection and parameter estim...
Technical ReportThe process of model learning can be considered in two stages: model selection and p...
The process of machine learning can be considered in two stages model selection and parameter estima...
In this paper we present a class of nonlinear neural network models and an associated learning algor...
This report presents a formalism that enables the dynamics of a broad class of neural networks to be...
Hamiltonian Monte Carlo is a widely used algorithm for sampling from posterior distributions of comp...
Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights w...
A gradient learning method to regulate the trajectories of some nonlinear chaotic systems is propose...
Transferring information from data to models is crucial to many scientific disciplines. Typically, t...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
Data-driven discovery of dynamics, where data is used to learn unknown dynamics, is witnessing a res...
Recently, there has been an increasing interest in modelling and computation of physical systems wit...
This paper deals with studying the asymptotical properties of multilayer neural networks models used...
We establish conditions under which Oja-Adams ’ learning models are gradient, semi-gradient, or grad...
The process of machine learning can be considered in two stages model selection and parameter estim...
The process of machine learning can be considered in two stages: model selection and parameter estim...
Technical ReportThe process of model learning can be considered in two stages: model selection and p...
The process of machine learning can be considered in two stages model selection and parameter estima...
In this paper we present a class of nonlinear neural network models and an associated learning algor...
This report presents a formalism that enables the dynamics of a broad class of neural networks to be...
Hamiltonian Monte Carlo is a widely used algorithm for sampling from posterior distributions of comp...
Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights w...
A gradient learning method to regulate the trajectories of some nonlinear chaotic systems is propose...
Transferring information from data to models is crucial to many scientific disciplines. Typically, t...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
Data-driven discovery of dynamics, where data is used to learn unknown dynamics, is witnessing a res...
Recently, there has been an increasing interest in modelling and computation of physical systems wit...
This paper deals with studying the asymptotical properties of multilayer neural networks models used...
We establish conditions under which Oja-Adams ’ learning models are gradient, semi-gradient, or grad...