Add to ORKGRestricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Le...
Working with any gradient-based machine learning algorithm involves the tedious task of tuning the o...
The nonlinear conjugate gradient method is widely used to solve unconstrained optimization problems....
This paper presents an optimization method for reducing the number of input channels and the complex...
Das Tutorium führt in die mathematischen Grundlagen von Klassifikationsproblemen mit Neuronalen Netz...
The paper presents an algorithm of adaptation by successive mesh regeneration and its application to...
In this paper we consider the possibility of computing rather than training the decision layer weigh...
We present a reduced basis Smagorinsky model. This model includes a non-linear eddy diffusion term t...
In this paper we study new preconditioners for the Nonlinear Conjugate Gradient (NCG) method in larg...
Derivative-free optimization (DFO) has enjoyed renewed interest over the past years, mostly motivate...
AbstractThe homotopy perturbation method is used to construct a new iteration algorithm for solving ...
Considers systems of linear equations, steepest ascent optimisation and Monte Carlo simulation
In this work we give su±cient conditions for k-th approximations of the polynomial roots of f(x) whe...
Nowadays, neural networks have represented a great step forward in the area of computing and artifi...
2000 Mathematics Subject Classification: 65H10.Here we give methodological survey of contemporary me...
Restricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Le...
Working with any gradient-based machine learning algorithm involves the tedious task of tuning the o...
The nonlinear conjugate gradient method is widely used to solve unconstrained optimization problems....
This paper presents an optimization method for reducing the number of input channels and the complex...
Das Tutorium führt in die mathematischen Grundlagen von Klassifikationsproblemen mit Neuronalen Netz...
The paper presents an algorithm of adaptation by successive mesh regeneration and its application to...
In this paper we consider the possibility of computing rather than training the decision layer weigh...
We present a reduced basis Smagorinsky model. This model includes a non-linear eddy diffusion term t...
In this paper we study new preconditioners for the Nonlinear Conjugate Gradient (NCG) method in larg...
Derivative-free optimization (DFO) has enjoyed renewed interest over the past years, mostly motivate...
AbstractThe homotopy perturbation method is used to construct a new iteration algorithm for solving ...
Considers systems of linear equations, steepest ascent optimisation and Monte Carlo simulation
In this work we give su±cient conditions for k-th approximations of the polynomial roots of f(x) whe...
Nowadays, neural networks have represented a great step forward in the area of computing and artifi...
2000 Mathematics Subject Classification: 65H10.Here we give methodological survey of contemporary me...
Restricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Le...
Working with any gradient-based machine learning algorithm involves the tedious task of tuning the o...
The nonlinear conjugate gradient method is widely used to solve unconstrained optimization problems....