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Showing 20 of 3,721 academic articles

A Survey on Neural Network Interpretability

Yu Zhang, Peter Tiňo B, Aleš Leonardis, Ke Tang • IEEE Transactions on Emerging Topics in Computational Intelligence (2021)

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,...

OPENALEX Open Access DOI: https://doi.org/10.1109/tetci.2021.3100641

Pointwise Convolutional Neural Networks

Binh‐Son Hua, Minh-Khoi Tran, Sai-Kit Yeung • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (2018)

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored....

OPENALEX Open Access DOI: https://doi.org/10.1109/cvpr.2018.00109

Neural Networks for Fingerprint Recognition

Pierre Baldi, Yves Chauvin • Neural Computation (1993)

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images...

OPENALEX DOI: https://doi.org/10.1162/neco.1993.5.3.402

Memristor devices for neural networks

Hongsik Jeong, Luping Shi • Journal of Physics D Applied Physics (2018)

Abstract Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfac...

OPENALEX cc-by DOI: https://doi.org/10.1088/1361-6463/aae223

Recurrent Neural Networks for Prediction

Danilo P. Mandic, Jonathon A. Chambers • Wiley eBooks (2001)

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) ...

OPENALEX DOI: https://doi.org/10.1002/047084535x

Neural networks in process fault diagnosis

T. Sorsa, Heikki N. Koivo, Hannu J. Koivisto • IEEE Transactions on Systems Man and Cybernetics (1991)

Fault detection and diagnosis is an important problem in process automation. Both model-based methods and expert systems have been suggested to solve the problem, along with the pattern recognition approach. A number of possible neural netw...

OPENALEX DOI: https://doi.org/10.1109/21.108299

State-of-the-art in artificial neural network applications: A survey

Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara, Kemi Victoria Dada et al. • Heliyon (2018)

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and ar...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.heliyon.2018.e00938 PMC: 6260436

A neural network approach to cloud classification

J. Lee, Ronald C. Weger, S.K. Sengupta, Ronald M. Welch • IEEE Transactions on Geoscience and Remote Sensing (1990)

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery ...

OPENALEX DOI: https://doi.org/10.1109/36.58972

Neural network computing

• Choice Reviews Online (1994)

At present, computers cannot think, make independent decisions, recognize visual and audible patterns, reach reliable conclusions without complete information or learn from their mistakes. The question this book and research into artificial...

OPENALEX DOI: https://doi.org/10.5860/choice.32-2176

Neural networks for switching

Timothy X. Brown • IEEE Communications Magazine (1989)

The author argues that a strong impetus for using neural networks is that they provide a framework for designing massively parallel machines. He notes that the highly interconnected architecture of switching networks suggests similarities t...

OPENALEX DOI: https://doi.org/10.1109/35.41403

A view of Artificial Neural Network

Manish Kumar Mishra, Monika Srivastava • International Conference on Advances in Engineering & Technology Research (2014)

In this paper, An Artificial Neural Network or ANN, its various characteristics and business applications. In this paper also show that “what are neural networks” and “Why they are so important in today's Artificial intelligence?” Because v...

OPENALEX DOI: https://doi.org/10.1109/icaetr.2014.7012785

Nonlinear Flight Control Using Neural Networks

Byoung S. Kim, Anthony J. Calise • Journal of Guidance Control and Dynamics (1997)

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...

OPENALEX DOI: https://doi.org/10.2514/2.4029

Neural networks: an introduction

Kevin Warwick • Institution of Engineering and Technology eBooks (1995)

In this chapter, neural networks are discussed. The present field of neural networks links a number of closely related areas, such as parallel distributed processing, connectionism and neural computing, these being brought together with the...

OPENALEX DOI: https://doi.org/10.1049/pbce053e_ch1