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Improved Equilibrium Optimizer for Accurate Training of Feedforward Neural Networks
AbstractOne of the most demanding applications of accurate Artificial Neural Networks (ANN) can be found in medical fields, mainly to make critical...
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Parameter identifiability of a deep feedforward ReLU neural network
The possibility for one to recover the parameters—weights and biases—of a neural network thanks to the knowledge of its function on a subset of the...
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Verifying Feedforward Neural Networks for Classification in Isabelle/HOL
Neural networks are being used successfully to solve classification problems, e.g., for detecting objects in images. It is well known that neural...
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Neural admittance control based on motion intention estimation and force feedforward compensation for human–robot collaboration
To enhance robotic manipulator adaptation to human partners and minimize human energy consumption in human–robot collaboration, this paper introduces...
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Feedforward Neural Networks
Artificial neural networks (ANN)s are collections of interconnected computation units modelled based on the neurons in the brain so that the program...
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Approximate blocked Gibbs sampling for Bayesian neural networks
In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of...
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Supervised Learning Neural Networks
In this chapter, we describe the basic concepts, notation, and basic learning algorithms for supervised neural networks that will be of great use for...
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High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network
In radar systems, target tracking errors are mainly from motion models and nonlinear measurements. When we evaluate a tracking algorithm, its...
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A dimensionality reduction approach for convolutional neural networks
The focus of this work is on the application of classical Model Order Reduction techniques, such as Active Subspaces and Proper Orthogonal...
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Comparing the efficiency of recurrent neural networks to EMG-based continuous estimation of the elbow angle
This study comprehensively assesses various recurrent neural networks (RNNs) for decoding the elbow angle from electromyogram (EMG) signals, a...
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Computational characteristics of feedforward neural networks for solving a stiff differential equation
Feedforward neural networks offer a possible approach for solving differential equations. However, the reliability and accuracy of the approximation...
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Quantum Neural Networks
Artificial neural networks, or simply neural networks, are a class of machine learning models defined by a structure of interconnected nodes, called...
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Feedforward Neural Networks
In this chapter, we will cover the most generic version of neural networks, feedforward neural networks. Feedforward neural networks are a group of...
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Siamese neural networks in recommendation
Recommender systems are widely adopted as an increasing research and development area, since they provide users with diverse and useful information...
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Bio-inspired interactive feedback neural networks for edge detection
In recent years, deep learning technology has significantly improved the performance of various computer vision tasks. Convolutional neural networks...
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Neural Networks
Neural networks are networks of nerve cells in the brains of humans and animals. The human brain has about 100 billion nerve cells. We, humans, owe...
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Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey
Over the past few years, larger and deeper neural network models, particularly convolutional neural networks (CNNs), have consistently advanced...
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Neural Networks
Logistic regression is an effective, but elementary technique. This chapter describes how we can extend it by stacking more layers and functions, and...
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An Intrusion Detection System Using Extended Kalman Filter and Neural Networks for IoT Networks
The unparalleled growth of the Internet of Things (IoT) is introducing a new paradigm shift in networking technology. By connecting everyday devices...
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On the computation of the gradient in implicit neural networks
Implicit neural networks and the related deep equilibrium models are investigated. To train these networks, the gradient of the corresponding loss...
