Add to ORKGIn the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified Linear Unit (ReLU), which became the most commonly used type of activation function in neural networks. That made certain types of network structure $\unicode{x2014}$such as the typical fully-connected feedforward neural network$\unicode{x2014}$ amenable to analysis through polyhedral theory and to the application of methodologies such as Linear Programming (LP) and...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
The neural network model (NN) comprised of relatively simple computing elements, operating in parall...
Multilayer neural networks were first proposed more than three decades ago, and various architecture...
Mechanistic interpretability aims to explain what a neural network has learned at a nuts-and-bolts l...
Deep Neural Networks (DNN) are well understood to be one of the largest consumers of HPC resources a...
Deep Neural Networks (DNNs) have revolutionized many aspects of our lives. The use of DNNs is becomi...
We contribute to a better understanding of the class of functions that can be represented by a neura...
Across scientific and engineering disciplines, the algorithmic pipeline forprocessing and understand...
Artificial neural networks are at the heart of some of the greatest advances in modern technology. T...
Abstract. In this paper we propose and investigate a novel nonlinear unit, called Lp unit, for deep ...
We propose an optimal architecture for deep neural networks of given size. The optimal architecture ...
In recent years, deep learning models have been widely used and are behind major breakthroughs acros...
We algorithmically determine the regions and facets of all dimensions of the canonical polyhedral co...
We contribute to a better understanding of the class of functions that is represented by a neural ne...
This book covers both classical and modern models in deep learning. The primary focus is on the theo...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
The neural network model (NN) comprised of relatively simple computing elements, operating in parall...
Multilayer neural networks were first proposed more than three decades ago, and various architecture...
Mechanistic interpretability aims to explain what a neural network has learned at a nuts-and-bolts l...
Deep Neural Networks (DNN) are well understood to be one of the largest consumers of HPC resources a...
Deep Neural Networks (DNNs) have revolutionized many aspects of our lives. The use of DNNs is becomi...
We contribute to a better understanding of the class of functions that can be represented by a neura...
Across scientific and engineering disciplines, the algorithmic pipeline forprocessing and understand...
Artificial neural networks are at the heart of some of the greatest advances in modern technology. T...
Abstract. In this paper we propose and investigate a novel nonlinear unit, called Lp unit, for deep ...
We propose an optimal architecture for deep neural networks of given size. The optimal architecture ...
In recent years, deep learning models have been widely used and are behind major breakthroughs acros...
We algorithmically determine the regions and facets of all dimensions of the canonical polyhedral co...
We contribute to a better understanding of the class of functions that is represented by a neural ne...
This book covers both classical and modern models in deep learning. The primary focus is on the theo...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
The neural network model (NN) comprised of relatively simple computing elements, operating in parall...
Multilayer neural networks were first proposed more than three decades ago, and various architecture...