14.5.9 Neural Networks

14.5.9.1 Neural Networks: General, Survey, Special Issues

van Veen, F.[Fjodor],
A mostly complete chart of Neural Networks,
Online
WWW Link. 2002
The Online link goes to Andrew Tch with explaination of each. The main reference is to the creator of the chart. BibRef

Deep Learning Tool Kit for Medical Imaging,
2017.
WWW Link. Code, Neural Networks. Neural networks toolkit written in python, on top of Tensorflow. Its modular architecture was developed to enable fast prototyping and ensure reproducibility in image analysis applications, with a particular focus on medical imaging.

Minsky, M.L.[Marvin L.], Papert, S.,
Perceptrons: An Introduction to Computational Geometry,
MIT PressCambridge, MA, 1969. Whey they do not work. BibRef 6900

Minsky, M.L.[Marvin L.], Selfridge, O.G.,
Learning in Random Nets,
IT60(335). BibRef 6000

Dewdney, A.K.,
Computer Recreations,
SciAmer(250), Month missing -- 1984, pp. 22-34. Perceptrons. Discussion of Perceptrons. BibRef 8400

Linsker, R.,
Self-organization in a perceptual network,
TC(21), 1988, pp. 105-117. 0907
BibRef

Pao, Y.H.,
Neural Net Computing for Pattern Recognition,
HPRCV97(Chapter I:4). (Case Western Reserve Univ.) BibRef 9700

Haykin, S.,
Neural Networks: A Comprehensive Introduction,
Prentice Hall1999. BibRef 9900

Skrzypek, J., Karplus, W., (Eds.)
Special Issue-Neural Networks in Vision and Pattern Recognition,
PRAI(6), No. 1, April 1992, pp. 1-208. BibRef 9204

Bischof, H.[Horst], Pinz, A.[Axel],
Artificial Versus Real Neural Networks,
BBS(15), No. 4, 1992, pp. 712. BibRef 9200

Guyun, I., Wang, P.S.P., (Eds.)
Special Issue on Advances in Pattern Recognition Using Neural Networks,
PRAI(8), No. 4, August 1993, pp. 645-963. BibRef 9308

Drucker, H., Schapire, R., Simard, P.Y.,
Boosting Performance in Neural Networks,
PRAI(7), 1993, pp. 705-719. BibRef 9300

Musavi, M.T., Chan, K.H., Hummels, D.M., Kalantri, K.,
On The Generalization Ability Of Neural Network Classifiers,
PAMI(16), No. 6, June 1994, pp. 659-663.
IEEE DOI BibRef 9406

Marshall, J.A.,
Adaptive Perceptual Pattern-Recognition by Self-Organizing Neural Networks: Context, Uncertainty, Multiplicity, and Scale,
NeurNet(8), No. 3, 1995, pp. 335-362. BibRef 9500

Sethi, I.K.,
Special Section on Artificial Neural Networks for Machine Vision,
MVA(8), No. 5, 1995, pp. 261-261.
Springer DOI BibRef 9500

Wang, S.S., Lin, W.G.,
A New Self-Organizing Neural Model for Invariant Pattern-Recognition,
PR(29), No. 4, April 1996, pp. 677-687.
Elsevier DOI BibRef 9604

Heikkonen, J., Bulsari, A.,
Special Issue on Neural Networks for Computer Vision Applications,
PRL(17), No. 4, April 4 1996, pp. 317-318. 9605
BibRef

Simes, E.D., Uebel, L.F., Augusto, D., Barone, C.,
Hardware Implementation of RAM Neural Networks,
PRL(17), No. 4, April 4 1996, pp. 421-429. 9605
BibRef

Bertin, E., Bischof, H., Bertolino, P.,
Voronoi Pyramids Controlled by Hopfield Neural Networks,
CVIU(63), No. 3, May 1996, pp. 462-475.
DOI Link 9606
BibRef
Earlier: A2, A1, A3:
Voronoi pyramids and Hopfield networks,
ICPR94(C:330-333).
IEEE DOI 9410
BibRef

Sethi, I.K., Yoo, J.H.,
Symbolic Mapping of Neurons in Feedforward Networks,
PRL(17), No. 10, September 2 1996, pp. 1035-1046. Connectionist. BibRef 9609

Chellappa, R., Fukushima, K., Katsaggelos, B.K., Kung, S.Y., Le Cun, Y.L., Nasrabadi, N.M., Poggio, T.A.,
Applications of Artificial Neural Networks to Image Processing,
IP(7), No. 8, August 1998, pp. 1093-1096.
IEEE DOI 9808
BibRef

Benediktsson, J.A., Sveinsson, J.R., Ersoy, O.K., Swain, P.H.,
Parallel Consensual Neural Networks,
TNN(8), No. 1, January 1997, pp. 54-64. 9701
BibRef

Mazza, C.,
Neural-Net Inference and Content-Addressable Memory,
TNN(8), No. 1, January 1997, pp. 133-140. 9701
BibRef

Atkinson, P.M., Tatnall, A.R.L.,
Neural Networks in Remote Sensing: Introduction,
JRS(18), No. 4, March 10 1997, pp. 699-709. 9703
BibRef

Wang, L.F.[Li-Feng], Cheng, H.D.,
Discretizing Continuous Neural Networks Using a Polarization Learning Rule,
PR(30), No. 2, February 1997, pp. 253-260.
Elsevier DOI 9704
BibRef

Willshaw, D., Hallam, J., Gingell, S., Lau, S.L.,
Marr Theory of the Neocortex as a Self-Organizing Neural-Network,
NeurComp(9), No. 4, May 15 1997, pp. 911-936. 9706
BibRef

Abdel-Wahhab, O.[Osama], Sid-Ahmed, M.A.,
A New Scheme for Training Feedforward Neural Networks,
PR(30), No. 3, March 1997, pp. 519-524.
Elsevier DOI 9705
BibRef

Pandya, A.S.[Abhijit S.], Macy, R.B.[Robert B.],
Pattern Recognition with Neural Networks in C++,
CRC PressBoca Raton, FL. 1996. Code, Neural Networks. ISBN 0-8493-9462-7. Complete code for the various algorithms. BibRef 9600

Frasconi, P., Gori, M., Soda, G.,
Links Between LVQ and Backpropagation,
PRL(18), No. 4, April 1997, pp. 303-310. 9708
BibRef

Hoekstra, A., Duin, R.P.W.,
Investigating Redundancy in Feedforward Neural Classifiers,
PRL(18), No. 11-13, November 1997, pp. 1293-1300. 9806
BibRef

de Ridder, D., Duin, R.P.W.,
Sammons Mapping Using Neural Networks: A Comparison,
PRL(18), No. 11-13, November 1997, pp. 1307-1316. 9806

See also nonlinear mapping for data structure analysis, A. BibRef

Yan, H., Gupta, M.M.,
Special Section on Neural Networks and Fuzzy Logic for Imaging Applications,
JEI(6), No. 3, July 1997, pp. 270-271. 9807
BibRef

Wilson, C.L., Blue, J.L., Omidvar, O.M.,
Neurodynamics of Learning and Network Performance,
JEI(6), No. 3, July 1997, pp. 379-385. 9807
BibRef

Wang, S.D., Hsu, T.C.,
Perceptron-Perceptron Net,
PRL(19), No. 7, May 1998, pp. 559-568. 9808
BibRef

Verikas, A.[Antanas], Lipnickas, A.[Arunas], Malmqvist, K.[Kerstin], Bacauskiene, M.[Marija], Gelzinis, A.[Adas],
Soft combination of neural classifiers: A comparative study,
PRL(20), No. 4, April 1999, pp. 429-444. BibRef 9904

Signahl, M.[Mikael], and Verikas, A.[Antanas],
Fuzzy Combination Schemes for Neural Networks,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Verikas, A., Bacauskiene, M.,
Feature selection with neural networks,
PRL(23), No. 11, September 2002, pp. 1323-1335.
Elsevier DOI 0206
BibRef

Bacauskiene, M., Verikas, A.,
Selecting salient features for classification based on neural network committees,
PRL(25), No. 16, December 2004, pp. 1879-1891.
Elsevier DOI 0411
BibRef

de Ridder, D., Duin, R.P.W., Verbeek, P.W., van Vliet, L.J.,
The Applicability of Neural Networks to Non-linear Image Processing,
PAA(2), No. 2, 1999, pp. 111-128. BibRef 9900

de Ridder, D., Duin, R.P.W., Verbeek, P.W., van Vliet, L.J.,
A Weight Set Decorrelating Algorithm for Neural Network Interpretation and Symmetry Breaking,
SCIA99(Neural Nets). BibRef 9900

Duin, R.P.W.[Robert P. W.], de Ridder, D.[Dick],
Neural network experiences between perceptrons and support vectors,
BMVC97(xx-yy).
HTML Version. 0209
BibRef

Kraaijveld, M.A., Duin, R.P.W.,
The effective capacity of multilayer feedforward network classifiers,
ICPR94(B:99-103).
IEEE DOI 9410
BibRef

Schmidt, W.F., Kraaijveld, M.A., Duin, R.P.W.,
Feedforward neural networks with random weights,
ICPR92(II:1-4).
IEEE DOI 9208
BibRef

Foody, G.M.[Giles M.],
The significance of border training patterns in classification by a feedforward neural network using back propagation learning,
JRS(20), No. 18, December 1999, pp. 3549. BibRef 9912

Asari, K.V.[K. Vijayan], Eswaran, C.,
Bidirectional multiple-valued neural network for pattern recognition and associative recall,
IJIST(11), No. 2, 2000, pp. 125-129. 0008
BibRef

Wang, B.Y.[Bao-Yun], He, Z.Y.[Zhen-Ya],
Can the classification capability of network be further improved by using quadratic sigmoidal neurons?,
PR(33), No. 8, August 2000, pp. 1395-1399.
Elsevier DOI 0005
BibRef

Hungenahally, S., and Bhattacharya, P.,
A Computational Approach to the Emulation of Visual Neural Architectures,
KBES(2), No. 3, 1998, pp. 185-193. BibRef 9800

Micheli-Tzanakou, E.[Evangelia],
Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence,
CRC PressJanuary 2000, ISBN 0-8493-2278-2. Review of current work. BibRef 0001

Chandra Kumar, P., Saratchandran, P., Sundararajan, N.,
Minimal radial basis function neural networks for nonlinear channel equalisation,
VISP(147), No. 5, October 2000, pp. 428-435. 0101
BibRef

Li, M.B., Huang, G.B., Saratchandran, P., Sundararajan, N.,
Complex-valued growing and pruning RBF neural networks for communication channel equalisation,
VISP(153), No. 4, August 2006, pp. 411-418.
DOI Link 0705
BibRef

Zhang, G.P.,
Neural networks for classification: a survey,
SMC-C(30), No. 4, November 2000, pp. 451-462.
IEEE Top Reference. 0104
Survey, Neural Networks. BibRef

Murino, V., Vernazza, G.,
Artificial Neural Networks for Image Analysis and Computer Vision,
IVC(19), No. 9-10, August 2001, pp. 583-584.
Elsevier DOI 0108
Special Issue introduction. BibRef

Raudys, S.J.[Sarunas J.],
Statistical and Neural Classifiers: An Integrated Approach to Design,
Springer-VerlagNew York, 2001. ISBN 1-85233-297-2. BibRef 0100

Egmont-Petersen, M., de Ridder, D., Handels, H.,
Image processing with neural networks: A Review,
PR(35), No. 10, October 2002, pp. 2279-2301.
Elsevier DOI 0206
Survey, Neural Networks. 200 applications. BibRef

Ripley, B.D.,
Pattern Recognition and Neural Networks,
Cambridge University Press1996. BibRef 9600

Ripley, B.D.,
Spatial Statistics,
Wiley1981, New York. BibRef 8100

Behnke, S.,
Hierarchical neural networks for image interpretation,
Springer2003, ISBN 3540407227.
PDF File. BibRef 0300

Sussner, P.[Peter], Graña, M.[Manuel],
Guest Editorial: Special Issue on Morphological Neural Networks,
JMIV(19), No. 2, September 2003, pp. 79-80.
DOI Link 0308
BibRef

Foresti, G.L., Dolso, T.,
An Adaptive High-Order Neural Tree for Pattern Recognition,
SMC-B(34), No. 2, April 2004, pp. 988-996.
IEEE Abstract. 0404
BibRef

Foresti, G.L., Christian, M., Snidaro, L.,
Adaptive high order neural trees for pattern recognition,
ICPR02(II: 877-880).
IEEE DOI 0211
BibRef

Rani, A.[Asha], Foresti, G.L.[Gian Luca], Micheloni, C.[Christian],
A neural tree for classification using convex objective function,
PRL(68, Part 1), No. 1, 2015, pp. 41-47.
Elsevier DOI 1512
Neural tree BibRef

Oh, K.S.[Kyoung-Su], Jung, K.C.[Kee-Chul],
GPU implementation of neural networks,
PR(37), No. 6, June 2004, pp. 1311-1314.
Elsevier DOI 0405
Graphics Processing Unit implementation of NN. BibRef

Wang, Z.B.[Zhao-Bin], Ma, Y.[Yide], Cheng, F.Y.[Fei-Yan], Yang, L.Z.[Li-Zhen],
Review of pulse-coupled neural networks,
IVC(28), No. 1, Januray 2010, pp. 5-13.
Elsevier DOI 1001
Pulse-coupled neural networks (PCNN); Image processing; Artificial neural network BibRef

Ma, Y.[Yide], Liu, L.[Li], Zhan, K.[Kun], Wu, Y.Q.[Yong-Qing],
Pulse-coupled neural networks and one-class support vector machines for geometry invariant texture retrieval,
IVC(28), No. 11, November 2010, pp. 1524-1529.
Elsevier DOI 1008
Pulse-coupled neural network (PCNN); Intersecting cortical model (ICM); Texture retrieval; Support vector machine (SVM); Feature extraction BibRef

Bengio, S.[Samy], Deng, L.[Li], Larochelle, H.[Hugo], Lee, H.L.[Hong-Lak], Salakhutdinov, R.[Ruslan],
Guest Editors' Introduction: Special Section on Learning Deep Architectures,
PAMI(35), No. 8, 2013, pp. 1795-1797.
IEEE DOI 1307
Computer architecture; Data mining; Data models; Learning systems; Neural networks; Signal processing algorithms; Special issues and sections BibRef

Bhattacharyya, S.[Siddhartha], Maulik, U.[Ujjwal],
Soft Computing for Image and Multimedia Data Processing,

Zhang, Y.N.[Yu-Nong], Yin, Y.H.[Yong-Hua], Guo, D.S.[Dong-Sheng], Yu, X.T.[Xiao-Tian], Xiao, L.[Lin],
Cross-validation based weights and structure determination of Chebyshev-polynomial neural networks for pattern classification,
PR(47), No. 10, 2014, pp. 3414-3428.
Elsevier DOI 1406
Cross validation BibRef

Ranzato, M.[Marc'Aurelio], Hinton, G.E.[Geoffrey E.], Le Cun, Y.L.[Yann L.],
Guest Editorial: Deep Learning,
IJCV(113), No. 1, May 2015, pp. 1-2.
Springer DOI 1506
BibRef

Romero, A.[Adriana], Radeva, P., Gatta, C.[Carlo],
Meta-Parameter Free Unsupervised Sparse Feature Learning,
PAMI(37), No. 8, August 2015, pp. 1716-1722.
IEEE DOI 1507
Encoding BibRef

Romero, A.[Adriana], Gatta, C.[Carlo],
Do We Really Need All These Neurons?,
IbPRIA13(460-467).
Springer DOI 1307
Restricted Boltzmann Machines (RBMs) are generative neural networks. BibRef

van der Velde, F.[Frank],
Computation and dissipative dynamical systems in neural networks for classification,
PRL(64), No. 1, 2015, pp. 44-52.
Elsevier DOI 1509
Classification BibRef

Miyajima, R.,
Deep Learning Triggers a New Era in Industrial Robotics,
MultMedMag(24), No. 4, October 2017, pp. 91-96.
IEEE DOI 1712
Cameras, Games, Industrial engineering, Intelligent systems, Machine learning, Pervasive computing, Service robots, software engineering BibRef

Lucas, A., Iliadis, M., Molina, R., Katsaggelos, A.K.,
Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods,
SPMag(35), No. 1, January 2018, pp. 20-36.
IEEE DOI 1801
Analytical models, Biological neural networks, Image reconstruction, Inverse problems, Machine learning, Visual systems BibRef

Gu, J.X.[Jiu-Xiang], Wang, Z.H.[Zhen-Hua], Kuen, J.[Jason], Ma, L.Y.[Lian-Yang], Shahroudy, A.[Amir], Shuai, B.[Bing], Liu, T.[Ting], Wang, X.X.[Xing-Xing], Wang, G.[Gang], Cai, J.F.[Jian-Fei], Chen, T.H.[Tsu-Han],
Recent advances in convolutional neural networks,
PR(77), 2018, pp. 354-377.
Elsevier DOI 1802
Convolutional neural network, Deep learning BibRef

Yang, L.P.[Li-Ping], MacEachren, A.M.[Alan M.], Mitra, P.[Prasenjit], Onorati, T.[Teresa],
Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review,
IJGI(7), No. 2, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Edwards, C.[Chris],
Deep Learning Hunts for Signals Among the Noise,
CACM(61), No. 6, June 2018, pp. 13-14.
DOI Link 1806
Trained neural networks can be tricked to focus on patterns in images that are barely noticeable by humans. BibRef

Deng, B.L., Li, G., Han, S., Shi, L., Xie, Y.,
Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey,
PIEEE(108), No. 4, April 2020, pp. 485-532.
IEEE DOI 2004
Compact neural network, data quantization, neural network acceleration, neural network compression, tensor decomposition BibRef

Que, Q.C.[Qi-Chao], Belkin, M.[Mikhail],
Back to the Future: Radial Basis Function Network Revisited,
PAMI(42), No. 8, August 2020, pp. 1856-1867.
IEEE DOI 2007
Kernel, Radial basis function networks, Training, Standards, Loss measurement, Training data, Supervised learning, k-means BibRef

Parhi, R., Nowak, R.D.,
The Role of Neural Network Activation Functions,
SPLetters(27), 2020, pp. 1779-1783.
IEEE DOI 2010
Splines (mathematics), Training, Biological neural networks, Green's function methods, Inverse problems, inverse problems BibRef

Pretorius, A.[Arnu], van Biljon, E.[Elan], van Niekerk, B.[Benjamin], Eloff, R.[Ryan], Reynard, M.[Matthew], James, S.[Steve], Rosman, B.[Benjamin], Kamper, H.[Herman], Kroon, S.[Steve],
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks,
PRL(138), 2020, pp. 95-105.
Elsevier DOI 2010
Neural networks, Critical initialisation, Signal propagation, Randomised control trial BibRef

Lin, R.[Ruiyuan], You, S.[Suya], Rao, R.[Raghuveer], Kuo, C.C.J.[C.C. Jay],
On Relationship of Multilayer Perceptrons and Piecewise Polynomial Approximators,
SPLetters(28), 2021, pp. 1813-1817.
IEEE DOI 2109
Neurons, Multilayer perceptrons, Tools, Piecewise linear approximation, Biological neural networks, piecewise polynomial approximation BibRef

Guo, Y.[Yiwen], Chen, L.[Long], Chen, Y.R.[Yu-Rong], Zhang, C.S.[Chang-Shui],
On Connections Between Regularizations for Improving DNN Robustness,
PAMI(43), No. 12, December 2021, pp. 4469-4476.
IEEE DOI 2112
Robustness, Jacobian matrices, Training data, Perturbation methods, Neural networks, Computational modeling, Task analysis, network property BibRef

Audibert, J.[Julien], Michiardi, P.[Pietro], Guyard, F.[Frédéric], Marti, S.[Sébastien], Zuluaga, M.A.[Maria A.],
Do deep neural networks contribute to multivariate time series anomaly detection?,
PR(132), 2022, pp. 108945.
Elsevier DOI 2209
Anomaly detection, Multivariate time series, Neural networks BibRef

Zhao, S.[Shuai], Zhou, L.[Liguang], Wang, W.X.[Wen-Xiao], Cai, D.[Deng], Lam, T.L.[Tin Lun], Xu, Y.S.[Yang-Sheng],
Toward Better Accuracy-Efficiency Trade-Offs: Divide and Co-Training,
IP(31), 2022, pp. 5869-5880.
IEEE DOI 2209
More networks (ensemble) is better than wider network. Training, Neural networks, Convolution, Costs, Tin, Kernel, Image classification, divide networks, co-training, deep networks ensemble BibRef

Han, Y.Z.[Yi-Zeng], Huang, G.[Gao], Song, S.[Shiji], Yang, L.[Le], Wang, H.H.[Hong-Hui], Wang, Y.L.[Yu-Lin],
Dynamic Neural Networks: A Survey,
PAMI(44), No. 11, November 2022, pp. 7436-7456.
IEEE DOI 2210
Computational modeling, Adaptation models, Adaptive systems, Routing, Deep learning, Training, Dynamic networks, convolutional neural networks BibRef

Peng, W.[Wei], Varanka, T.[Tuomas], Mostafa, A.[Abdelrahman], Shi, H.[Henglin], Zhao, G.Y.[Guo-Ying],
Hyperbolic Deep Neural Networks: A Survey,
PAMI(44), No. 12, December 2022, pp. 10023-10044.
IEEE DOI 2212
Mathematical models, Manifolds, Numerical models, Deep learning, Task analysis, Geometry, Computational modeling, Lorentz model BibRef

Sangalli, M.[Mateus], Blusseau, S.[Samy], Velasco-Forero, S.[Santiago], Angúlo, J.[Jesus],
Differential Invariants for SE(2)-Equivariant Networks,
ICIP22(2216-2220)
IEEE DOI 2211
Manifolds, Knowledge engineering, Adaptation models, Neural networks, Equivariant Neural Networks, Image Classification BibRef

Höfer, T.[Timon], Zell, A.[Andreas],
Automatic Adjustment of Fourier Embedding Parametrizations for Implicit Neural Representations,
ICPR22(2307-2313)
IEEE DOI 2212
Multilayer perceptrons, Iterative methods, Task analysis BibRef

Benbarka, N.[Nuri], Höfer, T.[Timon], Riaz, H.U.M.[Hamd Ul-Moqeet], Zell, A.[Andreas],
Seeing Implicit Neural Representations as Fourier Series,
WACV22(2283-2292)
IEEE DOI 2202
Training, Interpolation, Neural networks, Lattices, Multilayer perceptrons, Fourier series, 3D Computer Vision Implict neural representation BibRef

Melodia, L.[Luciano], Lenz, R.[Richard],
Estimate of the Neural Network Dimension Using Algebraic Topology and Lie Theory,
IMTA20(15-29).
Springer DOI 2103
BibRef

He, K., Girshick, R., Dollar, P.,
Rethinking ImageNet Pre-Training,
ICCV19(4917-4926)
IEEE DOI 2004
convolutional neural nets, image segmentation, iterative methods, learning (artificial intelligence), Object detection BibRef

Postels, J.[Janis], Ferroni, F.[Francesco], Coskun, H.[Huseyin], Navab, N.[Nassir], Tombari, F.[Federico],
Sampling-Free Epistemic Uncertainty Estimation Using Approximated Variance Propagation,
ICCV19(2931-2940)
IEEE DOI 2004
image segmentation, Monte Carlo methods, neural nets, sampling methods, deep neural networks, Real-time systems BibRef

Zamora Esquivel, J.[Julio], Cruz Vargas, J.A.[Jesus Adan], Lopez-Meyer, P.[Paulo],
Fractional Adaptation of Activation Functions In Neural Networks,
ICPR21(7544-7550)
IEEE DOI 2105
Training, Backpropagation, Network topology, Neurons, Radial basis function networks, Manuals, Topology BibRef

Zamora Esquivel, J., Cruz Vargas, A., Camacho Perez, R., Lopez Meyer, P., Cordourier, H., Tickoo, O.,
Adaptive Activation Functions Using Fractional Calculus,
NeruArch19(2006-2013)
IEEE DOI 2004
calculus, mathematical programming, multilayer perceptrons, radial basis function networks, RBF networks, fractional calculus BibRef

Majtner, T.[Tomáš], Nadimi, E.S.[Esmaeil S.],
Comparison of Deep Learning-Based Recognition Techniques for Medical and Biomedical Images,
CAIP19(I:492-504).
Springer DOI 1909
BibRef

Stockdill, A., Neshatian, K.,
Simulating neuromorphic reservoir computing: Abstract feed-forward hardware models,
IVCNZ17(1-7)
IEEE DOI 1902
feedforward neural nets, learning (artificial intelligence), memristors, neural chips, neural net architecture, Neurons BibRef

Cahill-Lane, J., Mills, S.,
Of mice, men, and machines: Real and artificial deep networks for vision,
IVCNZ17(1-6)
IEEE DOI 1902
medical image processing, neural nets, mice, men, artificial deep networks, artificial neural networks, Optical sensors BibRef

Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., Kalenichenko, D.,
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,
CVPR18(2704-2713)
IEEE DOI 1812
Quantization (signal), Training, Arrays, Computational modeling, Hardware, Neural networks BibRef

Park, E.[Eunhyeok], Yoo, S.[Sungjoo], Vajda, P.[Peter],
Value-Aware Quantization for Training and Inference of Neural Networks,
ECCV18(II: 608-624).
Springer DOI 1810
BibRef

Banerjee, S.[Samik], Bhattacharjee, P.[Prateep], Das, S.[Sukhendu],
Performance of Deep Learning Algorithms vs. Shallow Models, in Extreme Conditions - Some Empirical Studies,
PReMI17(565-574).
Springer DOI 1711
BibRef

Handa, A.[Ankur], Bloesch, M.[Michael], Patraucean, V.[Viorica], Stent, S.[Simon], McCormac, J.[John], Davison, A.[Andrew],
gvnn: Neural Network Library for Geometric Computer Vision,
DeepLearn16(III: 67-82).
Springer DOI 1611
Code, Neural Networks. BibRef

Nguyen, A.[Anh], Clune, J.[Jeff], Bengio, Y.[Yoshua], Dosovitskiy, A.[Alexey], Yosinski, J.[Jason],
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space,
CVPR17(3510-3520)
IEEE DOI 1711
Feature extraction, Generators, Image resolution, Neurons, Plugs, Probabilistic logic, Training BibRef

Nguyen, A.[Anh], Yosinski, J.[Jason], Clune, J.[Jeff],
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,
CVPR15(427-436)
IEEE DOI 1510
BibRef

Ben Othman, I.[Ibtissem], Ghorbel, F.[Faouzi],
Stability evaluation of neural and Bayesian classifiers: A new insight,
ICIP14(4314-4317)
IEEE DOI 1502
Artificial neural networks BibRef

Wu, F.[Fuke], Hu, S.G.[Shi-Geng],
Robust stability with general decay rate for stochastic neural networks with unbounded time-varying delays,
ICARCV12(753-758).
IEEE DOI 1304
BibRef

Karan, S., Majumder, D.D.,
Cognitive Quantum Number: The Logic for Nano Scale Information Processing in Minds and Machines,
NCVPRIPG11(183-186).
IEEE DOI 1205
BibRef

Madani, K.[Kurosh],
Artificial Neural Networks Based Image Processing and Pattern Recognition: From Concepts to Real-World Applications,
IPTA08(1-9).
IEEE DOI 0811
BibRef

Besdok, E.[Erkan],
Neurovision with Resilient Neural Networks,
Visual07(438-444).
Springer DOI 0706
BibRef

Giraudo, M.T.[Maria Teresa], Sacerdote, L.[Laura], Sicco, A.[Alessandro],
Ghost Stochastic Resonance for a Neuron with a Pair of Periodic Inputs,
BVAI07(398-407).
Springer DOI 0710
BibRef

Zanetti, B., Noriakilde, A., Saito, J.H.,
A Framework for Neural Networks Simulation and Visualization: Neocognitron Case,
ICIP05(III: 485-488).
IEEE DOI 0512
BibRef

Banarer, V.[Vladimir], Perwass, C.[Christian], Sommer, G.[Gerald],
Design of a Multilayered Feed-Forward Neural Network Using Hypersphere Neurons,
CAIP03(571-578).
Springer DOI 0311
BibRef

Silvestre, M.R., Ling, L.L.[Lee Luan],
Optimization of neural classifiers based on Bayesian decision boundaries and idle neurons pruning,
ICPR02(III: 387-390).
IEEE DOI 0211
BibRef

Li, Y.L.[Yan-Lai], Wang, K.Q.[Kuan-Quan], Zhang, D.,
Step acceleration based training algorithm for feedforward neural networks,
ICPR02(II: 84-87).
IEEE DOI 0211
BibRef

Toh, K.A.[Kar-Ann], Mao, K.Z.,
A global transformation approach to RBF neural network learning,
ICPR02(II: 96-99).
IEEE DOI 0211
BibRef

Grim, J., Pudil, P., Somol, P.,
Boosting in probabilistic neural networks,
ICPR02(II: 136-139).
IEEE DOI 0211
BibRef

Cardot, H., Lezoray, O.,
Graph of neural networks for pattern recognition,
ICPR02(II: 873-876).
IEEE DOI 0211
BibRef

Feiden, D., Tetzlaff, R.,
Iterative Annealing: a New Efficient Optimization Method for Cellular Neural Networks,
ICIP01(I: 549-552).
IEEE DOI 0108
BibRef

di Bona, S., Salvetti, O.,
An Efficient Method to Map a Regular Mesh Into a 3d Neural Network,
ICIP01(I: 529-532).
IEEE DOI 0108
BibRef

Wang, G.Y.[Guo-Yin],
Triple- or Multiple-Valued Logical Rule Generation from Neural Network,
ICPR98(ATP1). 9808
BibRef

Eigenmann, R., Nossek, J.A.,
Modification of Hard-Limiting Multilayer Neural Networks for Confidence Evaluation,
ICDAR97(1087-1091).
IEEE DOI 9708
BibRef
Earlier:
Constructive and Robust Combination of Perceptrons,
ICPR96(IV: 195-199).
IEEE DOI 9608
(Technical Univ. of Munich, D) BibRef

Utschick, W., Nossek, J.A.,
Bayesian Adaptation of Hidden Layers in Boolean Feedforward Neural Networks,
ICPR96(IV: 229-233).
IEEE DOI 9608
(Technical Univ. of Munich, D) BibRef

Lampinen, J.[Jouko], and Selonen, A.[Arto],
Using Background Knowledge in Multilayer Perceptron Learning,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Sardo, L., Kittler, J.V.[Josef V.],
Model Complexity Validation for PDF Estimation Using Gaussian Mixtures,
ICPR98(Vol I: 195-197).
IEEE DOI 9808
BibRef
Earlier:
Minimum Complexity PDF Estimation for Correlated Data,
ICPR96(II: 750-754).
IEEE DOI 9608
BibRef
And:
Complexity analysis of RBF networks for Pattern Recognition,
CVPR96(574-579).
IEEE DOI (Univ. of Surrey, UK) BibRef

Paik, J.H.[Jong-Hyun], Cho, S.B.[Sung-Bae], Lee, K.Y.[Kwan-Yong], Lee, Y.B.[Yill-Byung],
Multiple Recognizers System Using Two Stage Combinations,
ICPR96(IV: 581-585).
IEEE DOI 9608
(Yonsei Univ., KOR) BibRef

Ritter, G., Sussner, P.,
An Introduction to Morphological Neural Networks,
ICPR96(IV: 709-717).
IEEE DOI 9608
(Univ. of Florida, USA) BibRef

Kuncheva, L.I.[Ludmila I.], Hadjitodorov, S.,
An RBF Network with Tunable Function Shape,
ICPR96(IV: 645-649).
IEEE DOI 9608
(Imperial College of Science, UK) BibRef

Bayro-Corrochano, E., Buchholz, S., Sommer, G.,
A New Self-Organizing Neural Network Using Geometric Algebra,
ICPR96(IV: 555-559).
IEEE DOI 9608
(Christian Albrechts Univ., D) BibRef

Stoyanov, I.,
An Improved Backpropagation Neural Network Learning,
ICPR96(IV: 586-588).
IEEE DOI 9608
(Bulgarian Academy of Sciences, BG) BibRef

Wang, S., Zhu, X., Jin, Y.,
Multiple Experts Recognition System Based on Neural Network,
ICPR96(IV: 452-456).
IEEE DOI 9608
(Tshinghua Univ., PRC) BibRef

Vriesenga, M., Sklansky, J.,
Neural Modeling of Piecewise Linear Classifiers,
ICPR96(IV: 281-285).
IEEE DOI 9608
(Univ. of California, Irvine, USA) BibRef

Hamamoto, Y., Mitani, Y., Ishihara, H., Hase, T., Tomita, S.,
Evaluation of an Anti-Regularization Technique in Neural Networks,
ICPR96(IV: 205-208).
IEEE DOI 9608
(Yamaguchi Univ., J) BibRef

Chen, C.H., Jozwik, A.,
On the Small-Sample Behavior of the Class-Sensitive Neural Network,
ICPR96(IV: 209-213).
IEEE DOI 9608
(Univ. of Massachusetts, USA) BibRef

Bachelder, I.A.[Ivan A.], Gove, A.N.[Alan N.], Seibert, M.C.[Michael C.], and Waxman, A.M.[Allen M.],
From Learning Objects to Learning Environments: Biological and Computational Neural Systems,
ARPA94(II:871-883). BibRef 9400

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks Combinations and Evaluations .