Results 51 to 60 of about 9,085,112 (391)
Interacting neural networks [PDF]
Physical Review E, 2000 Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbour. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated.
Ido Kanter +2 more
openaire +4 more sources
Lecture Notes: Neural Network Architectures [PDF]
arXiv, 2023 These lecture notes provide an overview of Neural Network architectures from a mathematical point of view. Especially, Machine Learning with Neural Networks is seen as an optimization problem. Covered are an introduction to Neural Networks and the following architectures: Feedforward Neural Network, Convolutional Neural Network, ResNet, and Recurrent ...
arxiv
Deep Convolutional Neural Network for Inverse Problems in Imaging [PDF]
IEEE Transactions on Image Processing, 2016 In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades.
Kyong Hwan Jin +3 more
semanticscholar +1 more source
IEEE Transactions on Emerging Topics in Computational Intelligence, 2019 Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture.
Ayman Elgharabawy +2 more
openaire +7 more sources
Neural Plasticity, 2004 Functional and morphological studies in children affected by Attention Deficit Hyperactivity Disorder (ADHD) suggest a prefrontal cortex (PFc) dysfunction.
D. Viggiano +3 more
doaj +1 more source
Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression [PDF]
, 2023 We study the approximation capacity of some variation spaces corresponding to shallow ReLU$^k$ neural networks. It is shown that sufficiently smooth functions are contained in these spaces with finite variation norms. For functions with less smoothness, the approximation rates in terms of the variation norm are established.
arxiv +1 more source
Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks [PDF]
, 2023 Despite the many successful applications of deep learning models for multidimensional signal and image processing, most traditional neural networks process data represented by (multidimensional) arrays of real numbers. The intercorrelation between feature channels is usually expected to be learned from the training data, requiring numerous parameters ...
arxiv +1 more source
Neural Networks proposes to reconstruct situated practices, social histories, mediating techniques, and ontological assumptions that inform the computational project of the same name. If so-called machine learning comprises a statistical approach to pattern extraction, then neural networks can be defined as a biologically inspired model that relies on ...
Dhaliwal, Ranjodh Singh +2 more
openaire +2 more sources
Modelling of a post-combustion CO₂ capture process using neural networks [PDF]
, 2015 This paper presents a study of modelling post-combustion CO₂ capture process using bootstrap aggregated neural networks. The neural network models predict CO₂ capture rate and CO₂ capture level using the following variables as model inputs: inlet flue ...
Li, Fei +3 more
core +1 more source
Guaranteed Quantization Error Computation for Neural Network Model Compression [PDF]
arXiv, 2023 Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computation problem for neural network compression with quantization is addressed in this paper. A merged neural network is built from a feedforward neural network and its quantized
arxiv