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Academic Articles & Research Papers
Showing 20 of 608 academic articles
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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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
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
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 ...
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DOI: https://doi.org/10.1109/36.58972
Gongbo Zhang • IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
(2000)
Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifi...
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DOI: https://doi.org/10.1109/5326.897072
Sebastian Kujawa, Gniewko Niedbała • Agriculture
(2021)
Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple proc...
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cc-by
DOI: https://doi.org/10.3390/agriculture11060497
Jiahui Yu, Linjie Yang, Ning Xu, Yang, Jianchao et al. • arXiv (Cornell University)
(2018)
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...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1812.08928
Mike Schuster, Kuldip K. Paliwal • IEEE Transactions on Signal Processing
(1997)
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...
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DOI: https://doi.org/10.1109/78.650093
C. H. Chen • McGraw-Hill, Inc. eBooks
(1996)
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...
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J. T. Gene Hwang, Aidong Adam Ding • Journal of the American Statistical Association
(1997)
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...
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DOI: https://doi.org/10.1080/01621459.1997.10474027
Saad Albawi, Tareq Abed Mohammed, Saad Al-Azawi (2017)
The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...
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DOI: https://doi.org/10.1109/icengtechnol.2017.8308186
Laurene V. Fausett • CERN Document Server (European Organization for Nuclear Research)
(1994)
1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. Re...
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Hal S. Stern • Technometrics
(1996)
Artificial neural networks are computer algorithms or computer programs derived in part from attempts to model the activity of nerve cells. They have been applied to pattern recognition, classification, and optimization problems in the phys...
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DOI: https://doi.org/10.1080/00401706.1996.10484497
Brad Warner, Manavendra Misra • The American Statistician
(1996)
Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally be...
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DOI: https://doi.org/10.1080/00031305.1996.10473554
Abhay B. Bulsari • Elsevier eBooks
(1995)
Part 1 Theoretical issues: an introduction to artificial neural networks, D.T. Pham unsupervised neural learning, E. Oja back-propagation and its variations, D. Tsaptsinos the general approximation problem for feed-forward neural networks, ...
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Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1609.07061
David Faraggi, Richard M Simon • Statistics in Medicine
(1995)
Neural networks have received considerable attention recently, mostly by non-statisticians. They are considered by many to be very promising tools for classification and prediction. In this paper we present an approach to modelling censored...
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DOI: https://doi.org/10.1002/sim.4780140108
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji et al. • Proceedings of the AAAI Conference on Artificial Intelligence
(2019)
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for comp...
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Open Access
DOI: https://doi.org/10.1609/aaai.v33i01.33013558
Christos Christodoulou, Michael Georgiopoulos • CERN Document Server (European Organization for Nuclear Research)
(2001)
From the Book:
Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...
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