Academic Articles & Research Papers

Slimmable Neural Networks

We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individua...

Pattern Recognition and Neural Networks

Artificial Neural Networks

This chapter examines the history of artificial neural networks research through the present day. The components of artificial neural network architectures and both unsupervised and supervised learning methods are discussed. Although a step...

The power of quantum neural networks

Neural networks for system identification

Two approaches are presented for utilization of neural networks in identification of dynamical systems. In the first approach, a Hopfield network is used to implement a least-squares estimation for time-varying and time-invariant systems. T...

Multilayer perceptron and neural networks

Abstract:- The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning ...

Artificial neural networks enabled by nanophotonics

The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nano...

Bidirectional recurrent neural networks

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future f...

Fuzzy logic and neural network handbook

Fuzzy logic principles and algorithms design guidelines for fuzzy logic systems enhanced training algorithms maximum likelihood training temporal difference learning NADINE hybrid learning systems explanation and reasoning within connectivi...

Time Series Prediction and Neural Networks

Neural Networks and Their Applications

From the Publisher: The chapters in this volume are an indication of the vigour and strength of the subject of neural networks in solving hard problems. They also indicate how the subject is giving new insights and developing new relations...

Prediction Intervals for Artificial Neural Networks

The artificial neural network (ANN) is becoming a very popular model for engineering and scientific applications. Inspired by brain architecture, artificial neural networks represent a class of nonlinear models capable of learning from data...

Quantum convolutional neural networks

Nonlinear Flight Control Using Neural Networks

The theoretical developmentofa direct adaptivetracking controlarchitectureusingneuralnetworks ispresented. Emphasis is placed on utilization of neural networks in a  ight control architecture based on feedback linearization of the aircraft...

Artificial Neural Network Modelling

Application of Neural Networks

Self-Normalizing Neural Networks

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...

Graph neural networks

Neural Networks: A Review from a Statistical Perspective

This paper informs a statistical readership about Artificial Neural Networks (ANNs), points out some of the links with statistical methodology and encourages cross-disciplinary research in the directions most likely to bear fruit. The areas...

Lagrangian Neural Networks

Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the...