Home »
Academic Articles & Research Papers
Showing 20 of 608 academic articles
Bernd Jähne, Horst W. Haussecker, Peter Geißler (1999)
Contents of Volume One: Preface. Contributors. B. Jahne, Introduction. Illumination and Image Formation. H. Hausecker, Radiation. H. Hausecker, Interaction of Radiation with Matter. P. Geisler, Geometric and Wave Optics. H. Hausecker, Radio...
OPENALEX
Aleksey Viktorovich Skorokhodov, Sergey Vladimirovich Aksenov, Andrey Vladimirovich Aksenov, Dmitriy Nikolaevich Laykom • Известия Томского политехнического университета: Инжиниринг георесурсов
(2017)
The relevance of the research is caused by the necessity to develop algorithms and software to classify the cloud types based on single-layer cloud on the satellite images received from MODIS spectral radiometer used in Terra and Aqua remot...
DOAJ
Open Access (DOAJ)
Rafael Arthur Rocha Miranda, Francisco Assis da Silva, Mário Augusto Pazoti, Almir Olivette Artero et al. • Colloquium Exactarum
(2013)
The handwritten character recognition in digital images is an important and challenging area of
study in Computer Vision, with several possibilities for applications to facilitate the daily work of
the people. This paper presents an alg...
DOAJ
Open Access (DOAJ)
DOI: 10.5747/ce.2013.v05.n2.e062
Albert Nigrin • The MIT Press eBooks
(1993)
In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Networks for Pattern Recognition takes the pioneering work in artificial neural network...
OPENALEX
DOI: https://doi.org/10.7551/mitpress/4923.001.0001
Mohaiminul Islam, Guorong Chen, Shangzhu Jin • American Journal of Neural Networks and Applications
(2019)
Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in ...
OPENALEX
cc-by
DOI: https://doi.org/10.11648/j.ajnna.20190501.12
David M. Skapura (1995)
Foundations. Motivation. Neural-Network Fundamentals. Single Neuron Computations. Network Computations. Network Simulation. Foundations Summary. Suggested Readings. Bibliography. Paradigms. The Backpropagation Network. The Counterpropagatio...
OPENALEX
Itay Hubara, Daniel Soudry, Yaniv, Ran El • arXiv (Cornell University)
(2016)
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at train-time. We conduct two sets of experiments, each based on a...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1602.02505
Rui Zhang, Ranran Zhou, Zuting Zhong, Haifeng Qi et al. • Sensors
(2024)
Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCN...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s24227207
Kevin M. Swingler (1996)
Techniques for building neural networks: introduction what are neural networks? how does neural computing differ from traditional programming? how are neural networks built? how do neural networks learn? what do I need to build an MLP? the ...
OPENALEX
Samanwoy Ghosh‐Dastidar, Hojjat Adeli • International Journal of Neural Systems
(2009)
Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. A...
OPENALEX
DOI: https://doi.org/10.1142/s0129065709002002
Lars Kai Hansen, Peter Salamon • IEEE Transactions on Pattern Analysis and Machine Intelligence
(1990)
Several means for improving the performance and training of neural networks for classification are proposed. Crossvalidation is used as a tool for optimizing network parameters and architecture. It is shown that the remaining residual gener...
OPENALEX
Open Access
DOI: https://doi.org/10.1109/34.58871
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness et al. • Proceedings of the National Academy of Sciences
(2017)
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitabl...
OPENALEX
Open Access
DOI: https://doi.org/10.1073/pnas.1611835114
PMC: 5380101
Peter M. Atkinson, Adrian R. L. Tatnall • International Journal of Remote Sensing
(1997)
Abstract. Over the past decade there have been considerable increases in both the quantity of remotely sensed data available and the use of neural networks. These increases have largely taken place in parallel, and it is only recently that ...
OPENALEX
DOI: https://doi.org/10.1080/014311697218700
RT Pramod, Michael A Cohen, Joshua B Tenenbaum, Nancy Kanwisher • eLife
(2022)
Successful engagement with the world requires the ability to predict what will happen next. Here, we investigate how the brain makes a fundamental prediction about the physical world: whether the situation in front of us is stable, and henc...
DOAJ
Open Access (DOAJ)
DOI: 10.7554/eLife.71736
Johann Gasteiger, Jure Zupan • Angewandte Chemie International Edition in English
(1993)
Abstract The capabilities of the human brain have always fascinated scientists and led them to investigate its inner workings. Over the past 50 years a number of models have been developed which have attempted to replicate the brain's vario...
OPENALEX
DOI: https://doi.org/10.1002/anie.199305031
Kevin L. Moore • IEEE Potentials
(1992)
Artificial neural networks are explained, and the different types are described. Three different tasks for which they are suitable are discussed. They are pattern classification and associative memory, self-organization and feature extracti...
OPENALEX
DOI: https://doi.org/10.1109/45.127697
H. K. D. H. Bhadeshia • ISIJ International
(1999)
There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach o...
OPENALEX
Open Access
DOI: https://doi.org/10.2355/isijinternational.39.966
Alex Warstadt, Amanpreet Singh, Samuel R. Bowman • Transactions of the Association for Computational Linguistics
(2019)
This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set ...
OPENALEX
cc-by
DOI: https://doi.org/10.1162/tacl_a_00290
Christoph Bergmeir, José M. Benítez • Journal of Statistical Software
(2012)
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...
OPENALEX
cc-by
DOI: https://doi.org/10.18637/jss.v046.i07
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger • arXiv (Cornell University)
(2017)
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...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1706.04599