We investigate analog neural networks. They have continuous state variables that depend continuously on time. Although they all have an energy function, not all can have their dynamics derived from a Hamiltonian. Some necessary conditions are given for the network to have Hamiltonian dynamics. We give an example and, using symplectic transformations, describe a whole class of neural networks with Hamiltonian dynamics
AbstractWe pursue a particular approach to analog computation, based on dynamical systems of the typ...
Some classes of dissipative and Hamiltonian distributed systems are described. The dynamics of these...
This paper demonstrates the capabilities of Convolutional Neural Networks (CNNs) at classifying type...
This report presents a formalism that enables the dynamics of a broad class of neural networks to be...
... this paper we present the alternate view that brains are (in part) dynamic simulation devices ca...
In this paper we present a class of nonlinear neural network models and an associated learning algor...
The process of machine learning can be considered in two stages model selection and parameter estim...
Pre-PrintThe process of machine learning can be considered in two stages model selection and paramet...
The process of model learning can be considered in two stages: model selection and parameter estimat...
Recently, there has been an increasing interest in modelling and computation of physical systems wit...
A transform is introduced that maps discrete neural network dynamics to almost everywhere topologica...
Understanding how the dynamics of a neural network is shaped by the network structure and, consequen...
Because the dynamics of a neural network with symmetric interactions is similar to a gradient descen...
We pursue a particular approach to analog computation, based on dynamical systems of the type used i...
The process of machine learning can be considered in two stages: model selection and parameter estim...
AbstractWe pursue a particular approach to analog computation, based on dynamical systems of the typ...
Some classes of dissipative and Hamiltonian distributed systems are described. The dynamics of these...
This paper demonstrates the capabilities of Convolutional Neural Networks (CNNs) at classifying type...
This report presents a formalism that enables the dynamics of a broad class of neural networks to be...
... this paper we present the alternate view that brains are (in part) dynamic simulation devices ca...
In this paper we present a class of nonlinear neural network models and an associated learning algor...
The process of machine learning can be considered in two stages model selection and parameter estim...
Pre-PrintThe process of machine learning can be considered in two stages model selection and paramet...
The process of model learning can be considered in two stages: model selection and parameter estimat...
Recently, there has been an increasing interest in modelling and computation of physical systems wit...
A transform is introduced that maps discrete neural network dynamics to almost everywhere topologica...
Understanding how the dynamics of a neural network is shaped by the network structure and, consequen...
Because the dynamics of a neural network with symmetric interactions is similar to a gradient descen...
We pursue a particular approach to analog computation, based on dynamical systems of the type used i...
The process of machine learning can be considered in two stages: model selection and parameter estim...
AbstractWe pursue a particular approach to analog computation, based on dynamical systems of the typ...
Some classes of dissipative and Hamiltonian distributed systems are described. The dynamics of these...
This paper demonstrates the capabilities of Convolutional Neural Networks (CNNs) at classifying type...
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