Academic Articles & Research Papers

Elements of Artificial Neural Networks

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have ...

Neuro-Evolution of Continuous-Time Dynamic Process Controllers

Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised...

Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main feat...

An Introduction to Convolutional Neural Networks

The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of art...

Neural Networks: A Comprehensive Foundation

From the Publisher: This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emer...

Artificial Neural Networks

An artificial neural network (ANN) is a functional abstraction of the biologic neural structures of the central nervous system (Aleksander and Morton 1993; Rudomin et al. 1993; Arbib 1995; Anderson 1995). It is essentially a simple mathemat...

On Calibration of Modern Neural Networks

Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike thos...

ACTIVATION FUNCTIONS IN NEURAL NETWORKS

Artificial Neural Networks are inspired from the human brain and the network of neurons present in the brain.The information is processed and passed on from one neuron to another through neuro synaptic junctions.Similarly, in artificial neu...

Fundamentals of Artificial Neural Networks

From the Publisher: As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Funda...

Neural Network Learning

First published in 1999, this book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. R...

Fast Fourier Transform Networks for Object Tracking Based on Correlation Filter

In this paper, we propose fast Fourier transform networks for object tracking, called FFTNet. FFTNet is a correlation filter (CF)-based tracker that integrates two main components of CF, i.e., auto correlation and cross correlation between ...

CL-BPUWM: continuous learning with Bayesian parameter updating and weight memory

Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the pa...

Applications of deep learning in dentistry

Deep Learning for Sensor-based Human Activity Recognition

The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in pra...

Deep learning in electron microscopy

Abstract Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applic...

Deep learning in nano-photonics: inverse design and beyond

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. Many of the recent works on machine-learning inverse design are highly specific, and the...

Deep Learning in Image Cytometry: A Review

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and ho...

Deep learning in computational mechanics: a review

Abstract The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies w...

Formative assessment: a key to deep learning?

A paradigm shift in assessment culture has emphasized the importance of formative assessment. The existing evidence supports the identification of feedback as the central component of formative assessment. Feedback provides information abou...

On optimization methods for deep learning

The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to dev...