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On generalization by neural networks
1998
Information Sciences
Preserved Fulltext
We report new results on the corner classification approach to training feedforward neural networks. ...
It is shown that a prescriptive learning procedure where the weights are simply read off based on the training data can provide good generalization. ...
This research was partly supported by NASA. ...
doi:10.1016/s0020-0255(98)10009-9
fatcat:6yqqtvzyvnbnlf5rfdmnbccg74
A Note on the Regularity of Images Generated by Convolutional Neural Networks
[article]
2022
arXiv
pre-print
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The regularity of images generated by convolutional neural networks, such as the U-net, generative adversarial networks, or the deep image prior, is analyzed. ...
While such statements require an infinite dimensional setting, the connection to (discretized) neural networks used in practice is made by considering the limit as the resolution approaches infinity. ...
Application to Neural Networks Operating on Pixel Grids In this section we bridge the gap between the above introduced function space CNNs and the discrete CNNs used in practice. ...
arXiv:2204.10588v1
fatcat:y6znpawgm5apbeauct6dc5oegq
Forecasting solar power generated by grid connected PV systems using ensembles of neural networks
2015
2015 International Joint Conference on Neural Networks (IJCNN)
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We propose three different approaches based on ensembles of neural networks -two non-iterative and one iterative. ...
In this paper, we study the application of neural networks for predicting the next day photovoltaic power outputs in 30 minutes intervals from the previous values, without using any exogenous data. ...
ACKNOWLEDGMENT This research was partially supported by a research award from the Clean Energy and Intelligent Networks Research Cluster at the University of Sydney. ...
doi:10.1109/ijcnn.2015.7280574
dblp:conf/ijcnn/RanaKA15
fatcat:atw3asql7bdutmt675nzpomc2i
DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method
[article]
2022
arXiv
pre-print
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We introduce the so called DeepParticle method to learn and generate invariant measures of stochastic dynamical systems with physical parameters based on data computed from an interacting particle method ...
We utilize the expressiveness of deep neural networks (DNNs) to represent the transform of samples from a given input (source) distribution to an arbitrary target distribution, neither assuming distribution ...
Acknowledgements The research of JX is partially supported by NSF grants DMS-1924548 ...
arXiv:2111.01356v3
fatcat:atl7vpps4faapnbc3535vaz4ri
Graph generation by sequential edge prediction
2019
The European Symposium on Artificial Neural Networks
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Here, we propose a recurrent Deep Learning based model to generate graphs by learning to predict their ordered edge sequence. ...
models from graph theory, and reaching performances comparable to the current state of the art on graph generation. ...
to employ reliable Recurrent Neural Network architectures (often included in the Deep Learning framework) for learning and generation. ...
dblp:conf/esann/BacciuMP19
fatcat:zdnj56d2cjgjnie6472tk25gue
On End-to-End Program Generation from User Intention by Deep Neural Networks
[article]
2015
arXiv
pre-print
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This paper envisions an end-to-end program generation scenario using recurrent neural networks (RNNs): Users can express their intention in natural language; an RNN then automatically generates corresponding ...
code in a characterby-by-character fashion. ...
Such standard pipelines might be generated automatically by neural networks, provided context code. ...
arXiv:1510.07211v1
fatcat:gmmz2vceybe5liqes5ke5f7zxu
Multispectral image characterization by partial generalized covariance
2011
The European Symposium on Artificial Neural Networks
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Depending on the choice of measure different spectral features get highlighted by attribute assessment, this way creating new image segmentation aspects, as shown in a comparison of Euclidean distance, ...
general similarity measures. ...
Research is founded by DFG Graduiertenkolleg 1564 'Imaging new Modalities'. ...
dblp:conf/esann/StrickertLKV11
fatcat:uhalsoafirhslnind3aajlvb2y
Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm
2015
PLoS ONE
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To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies ...
The RBF neural network is similar to the BP neural network. Both networks approach the error by adjusting the weights of neurons [12] . ...
Materials and Methods
General regression neural network (GRNN) GRNN is a radial basis function neural network that is composed of an input layer, pattern layer, summation layer and output layer. ...
doi:10.1371/journal.pone.0120976
pmid:25807466
pmcid:PMC4373783
fatcat:coaza4enxzfa7or7qhpfguvtqu
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
2019
The World Wide Web Conference on - WWW '19
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2019 pre-print arXiv:1904.04447v1 fatcat:dq6mgjdnmjgp7j5ymwvihar5yi
In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. ...
Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. ...
FEATURE GENERATION BY CONVOLUTIONAL NEURAL NETWORK MODEL 2.1 Overview In this section, we will describe the proposed Feature Generation by Convolutional Neural Network (FGCNN) model in detail. ...
doi:10.1145/3308558.3313497
dblp:conf/www/LiuTCYGZ19
fatcat:gwfmax3u6zbtxljld5fbhubnhi
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
2001
IEEE transactions on fuzzy systems
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In this paper, a fast approach for automatically generating fuzzy rules from sample patterns using generalized dynamic fuzzy neural networks (GD-FNNs) is presented. ...
quickly without resorting to the backpropagation (BP) iteration learning, a common approach adopted by many existing methods. ...
Recently, more attentions have been focused on fuzzy neural networks (FNNs) to acquire fuzzy rules based on the learning ability of neural networks [9] . ...
doi:10.1109/91.940970
fatcat:edln2ysf2nb6jioklpcvbijklm
Investigation of generalized Hopfield model by statistical physics methods
2010
The 2010 International Joint Conference on Neural Networks (IJCNN)
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The proposed generalization of the Hopfield model consists in assigning different weight coefficients to input patterns that are used to construct the Hebb connection matrix. ...
The Hopfield model is a basic model of associative neural networks. ...
It was found out that the modifying of only one weight could principally change the network properties. ...
doi:10.1109/ijcnn.2010.5596872
dblp:conf/ijcnn/KryzhanovskyL10
fatcat:bruikt346jasxbagztlxv5in2u
Remote Control of Respiratory Neural Network by Spinal Locomotor Generators
2014
PLoS ONE
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In addition, this locomotion-induced respiratory rhythm modulation is prevented both by bilateral lesion of the pFRG region and by blockade of neurokinin 1 receptors in the brainstem. ...
The extent to which direct central interactions between the spinal networks controlling locomotion and the brainstem networks controlling breathing are involved in this rhythm modulation remains unknown ...
locomotor and respiratory neural networks. ...
doi:10.1371/journal.pone.0089670
pmid:24586951
pmcid:PMC3930745
fatcat:fsh2kxx6ynbqzjld24owor5ur4
Distributed generation dispatch optimization by artificial neural network trained by particle swarm optimization algorithm
2011
2011 8th International Conference on the European Energy Market (EEM)
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Researches in artificial neural network field are based on different network architectures including multilayer perceptron, single multiplicative neuron and pi-sigma neuron model. ...
To obtain a satisfactory performance for these classifiers, one of the most important issues is network training. Evolutionary algorithms are commonly used for training neural network classifiers. ...
Network Pi-sigma neural network is higher order feedforward introduced by [8] . ...
doi:10.1109/eem.2011.5953071
fatcat:h6h3jcdtefbt5cvrz7kin5me2q
Defect Detection and Classification by Training a Generic Convolutional Neural Network Encoder
2020
IEEE Transactions on Signal Processing
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To address this problem we generate large numbers of synthetic images by combining real defects with different backgrounds. ...
We propose a method for locating and classifying abnormalities using Convolutional Neural Networks (CNNs). ...
The Titan Xp used for this research was donated by the NVIDIA Corporation. ...
doi:10.1109/tsp.2020.3031188
fatcat:m54rmyjnibhatca3xfobwhqmku
Efficient Minimax Clustering Probability Machine by Generalized Probability Product Kernel
2008
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
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Experimental results on synthetic and real data validate the effectiveness of MCPM for classification while attaining high accuracy. ...
Following many traditional methods, we represent training data points by several clusters. ...
Downloaded on September 17, 2009 at 06:12 from IEEE Xplore. Restrictions apply.
International Joint Conference on Neural Networks(IJCNN 2008) ...
doi:10.1109/ijcnn.2008.4634375
dblp:conf/ijcnn/YangHKL08
fatcat:tyev3euutjb3plrnjtfttbuvge
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