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Academic Articles & Research Papers
Showing 20 of 2,356 academic articles
R. Daniel VanLuchene, ROUFEI SUN • Computer-Aided Civil and Infrastructure Engineering
(1990)
In the past few years literature on computational civil engineering has concentrated primarily on artificial intelligence (Al) applications involving expert system technology. This article discusses a different Al approach involving neural ...
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cc-by-nc-nd
DOI: https://doi.org/10.1111/j.1467-8667.1990.tb00377.x
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.,...
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Open Access
DOI: https://doi.org/10.1109/tetci.2021.3100641
Igor I. Baskin, David Alan Winkler, Igor V. Tetko • Expert Opinion on Drug Discovery
(2016)
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...
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DOI: https://doi.org/10.1080/17460441.2016.1201262
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....
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Open Access
DOI: https://doi.org/10.1109/cvpr.2018.00109
Mikhail Yurochkin, Mayank Agarwal, Ghosh, Soumya, Kristjan H. Greenewald et al. • arXiv (Cornell University)
(2019)
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1905.12022
Tommy W. S. Chow, Siu‐Yeung Cho • Series in electrical and computer engineering
(2007)
This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been co...
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DOI: https://doi.org/10.1142/p487
Qeethara Kadhim Al-Shayea (2011)
Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; da...
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Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler et al. • arXiv (Cornell University)
(2018)
Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1806.08734
W.D. Penny, David Frost • Medical Decision Making
(1996)
Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical de...
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DOI: https://doi.org/10.1177/0272989x9601600409
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...
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cc-by
DOI: https://doi.org/10.1088/1361-6463/aae223
Alison Carling • Sigma Press eBooks
(1992)
A background to the history of neural networks is provided in this book, in a way accessible to readers without a formal knowledge of computer science or advanced biology. It describes the major learning algorithms, the constraints of neuro...
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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) ...
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DOI: https://doi.org/10.1002/047084535x
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann • arXiv (Cornell University)
(2018)
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1805.09112
Richard J. Mammone (1994)
Neural Networks for speech processing. Neural Networks in the acquisition of speech by machine. The nervous system - fantasy and reality. Processing of complex stimuli in the mammalian cochlear nucleus. On the possible role of auditory peri...
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N.K. Bose, Pengfei Liang (1995)
Part 1 Fundamentals: basics of neuroscience and artificial neuron models graphs algorithms. Part 2 Feedforward networks: perceptrons and LMS algorithm complexity of learning using feedforward networks adaptive structure networks. Part 3 Rec...
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Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami et al. • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
(2019)
Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...
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Open Access
DOI: https://doi.org/10.1145/3292500.3330961
Donald F. Specht • IEEE Transactions on Neural Networks
(1991)
A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with...
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DOI: https://doi.org/10.1109/72.97934
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele et al. • arXiv (Cornell University)
(2018)
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1808.04260
Nicholas Carlini, David Wagner • IEEE Symposium on Security and Privacy (S&P)
(2017)
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x'...
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Open Access
DOI: https://doi.org/10.1109/sp.2017.49
Stephan Rasp, Sebastian Lerch • Monthly Weather Review
(2018)
Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the pa...
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other-oa
DOI: https://doi.org/10.1175/mwr-d-18-0187.1