2In the framework of discontinuous function approximation and discontinuity interface detection, we consider an approach involving Neural Networks. In particular, we define a novel typology of Neural Network layers endowed with new learnable parameters and discontinuities in the space of the activations. These layers allow to create a completely new kind of Neural Networks, characterized to be discontinuous, not only able to approximate discontinuous functions but also to learn and detect the discontinuity interfaces. A sound theoretical analysis concerning the properties of the new discontinuous layers is performed, and some tests on discontinuous functions are proposed, in order to assess the potential of such instruments.noneopendella sa...
In this paper, we propose a cusp-capturing physics-informed neural network (PINN) to solve discontin...
A new strategy for incremental building of multilayer feedforward neural networks is proposed in the...
Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problem...
In this paper, a new Discontinuity Capturing Shallow Neural Network (DCSNN) for approximating $d$-di...
Most stochastic gradient descent algorithms can optimize neural networks that are sub-differentiable...
Most stochastic gradient descent algorithms can optimize neural networks that are sub-differentiable...
This paper studies the computational power of various discontinuous real computational models that a...
This book presents as its main subject new models in mathematical neuroscience. A wide range of neur...
In this paper a neural network for approximating continuous and discontinuous mappings is described....
This book presents as its main subject new models in mathematical neuroscience. A wide range of neur...
The multiplicity of approximation theorems for Neural Networks do not relate to approximation of lin...
The focus of this paper is on the neural network modelling approach that has gained increasing recog...
This article studies the computational power of various discontinuous real computational models that...
The focus of this paper is on the neural network modelling approach that has gained increasing recog...
International audienceWe study the expressivity of deep neural networks. Measuring a network's compl...
In this paper, we propose a cusp-capturing physics-informed neural network (PINN) to solve discontin...
A new strategy for incremental building of multilayer feedforward neural networks is proposed in the...
Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problem...
In this paper, a new Discontinuity Capturing Shallow Neural Network (DCSNN) for approximating $d$-di...
Most stochastic gradient descent algorithms can optimize neural networks that are sub-differentiable...
Most stochastic gradient descent algorithms can optimize neural networks that are sub-differentiable...
This paper studies the computational power of various discontinuous real computational models that a...
This book presents as its main subject new models in mathematical neuroscience. A wide range of neur...
In this paper a neural network for approximating continuous and discontinuous mappings is described....
This book presents as its main subject new models in mathematical neuroscience. A wide range of neur...
The multiplicity of approximation theorems for Neural Networks do not relate to approximation of lin...
The focus of this paper is on the neural network modelling approach that has gained increasing recog...
This article studies the computational power of various discontinuous real computational models that...
The focus of this paper is on the neural network modelling approach that has gained increasing recog...
International audienceWe study the expressivity of deep neural networks. Measuring a network's compl...
In this paper, we propose a cusp-capturing physics-informed neural network (PINN) to solve discontin...
A new strategy for incremental building of multilayer feedforward neural networks is proposed in the...
Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problem...