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Academic Articles & Research Papers
Showing 20 of 4,247 academic articles
Sonit Singh • arXiv (Cornell University)
(2018)
With rise of digital age, there is an explosion of information in the form of news, articles, social media, and so on. Much of this data lies in unstructured form and manually managing and effectively making use of it is tedious, boring and...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1807.02383
Kevin Crowston, Eileen E. Allen, Robert Heckman • International Journal of Social Research Methodology
(2011)
Social researchers often apply qualitative research methods to study groups and their communications artifacts. The use of computer-mediated communications has dramatically increased the volume of text available, but coding such text requir...
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DOI: https://doi.org/10.1080/13645579.2011.625764
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During t...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1602.02830
Peter L. Bartlett, Dylan J. Foster, Matus Telgarsky • arXiv (Cornell University)
(2017)
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrice...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1706.08498
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jong‐Seok Lee et al. • Artificial Intelligence Review
(2023)
Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...
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cc-by
DOI: https://doi.org/10.1007/s10462-023-10562-9
Ian Parberry, Michael R. Garey, Albert R. Meyer • The MIT Press eBooks
(1994)
Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how wel...
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DOI: https://doi.org/10.7551/mitpress/1836.001.0001
Jianlin Cheng, Zheng Wang, Gianluca Pollastri • International Joint Conference on Neural Networks (IJCNN)
(2008)
Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a gen...
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Open Access
DOI: https://doi.org/10.1109/ijcnn.2008.4633963
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
Vivek Prakash Nigam, Daniel Graupe • Neurological Research
(2004)
Diagnosis of epilepsy is primarily based on scalp-recorded electroencephalograms (EEG). Unfortunately the long-term recordings obtained from 'ambulatory recording systems' contain EEG data of up to one week duration, which has introduced ne...
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DOI: https://doi.org/10.1179/016164104773026534
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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Nachimuthu Karunanithi, Darrell Whitley, Yashwant K. Malaiya • IEEE Software
(1992)
It is shown that neural network reliability growth models have a significant advantage over analytic models in that they require only failure history as input and not assumptions about either the development environment or external paramete...
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DOI: https://doi.org/10.1109/52.143107
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
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...
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DOI: https://doi.org/10.1162/neco.1993.5.3.402
Pooja Jain, Makarand Chintamani Deo • Ships and Offshore Structures
(2005)
The soft computing technique of neural network is being extensively used across all disciplines of ocean engineering, namely, offshore, coastal, and deep-ocean engineering including marine engineering. This paper takes a stock of the resear...
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DOI: https://doi.org/10.1533/saos.2004.0005
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