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2004
convolutional neural nets, learning (artificial intelligence),
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computer vision, learning (artificial intelligence),
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1812
Transforms, Neural networks, Needles, Upper bound, Training, Plastics,
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CVPR18(4376-4384)
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1812
Quantization (signal), Hardware, Biological neural networks,
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Towards a Mathematical Understanding of the Difficulty in Learning
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1812
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1810
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1810
Benchmarks 16 combinations of popular Deep Neural Networks for 1000-category
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1810
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ECCV18(XI: 181-195).
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1810
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1810
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1810
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ConvNets and ImageNet Beyond Accuracy:
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1810
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WACV18(1794-1803)
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1806
feature extraction, image segmentation, inference mechanisms,
learning (artificial intelligence), neural nets,
Visualization
BibRef
Goh, G.B.,
Siegel, C.,
Vishnu, A.,
Hodas, N.,
Baker, N.,
How Much Chemistry Does a Deep Neural Network Need to Know to Make
Accurate Predictions?,
WACV18(1340-1349)
IEEE DOI
1806
computer vision, free energy, image representation,
learning (artificial intelligence), neural nets, CNN models,
Task analysis
BibRef
Yuan, B.,
Chen, J.,
Zhang, W.,
Tai, H.S.,
McMains, S.,
Iterative Cross Learning on Noisy Labels,
WACV18(757-765)
IEEE DOI
1806
incorrect labels in training data.
convolution, feedforward neural nets, image classification,
iterative methods, Iterative Cross Learning,
Training data
BibRef
Novotny, D.[David],
Larlus, D.[Diane],
Vedaldi, A.[Andrea],
I Have Seen Enough: Transferring Parts Across Categories,
BMVC16(xx-yy).
HTML Version.
1805
Whether further progress can be indefinitely sustained by annotating
more data, or whether there is a saturation point beyond which a
problem is essentially solved, or the capacity of a model is
saturated.
A few thousand examples.
BibRef
Stabinger, S.,
Rodríguez-Sánchez, A.,
Evaluation of Deep Learning on an Abstract Image Classification
Dataset,
CogCV17(2767-2772)
IEEE DOI
1802
Cameras, Concrete, Machine learning, Training
BibRef
Hou, S.H.[Sai-Hui],
Liu, X.[Xu],
Wang, Z.L.[Zi-Lei],
DualNet: Learn Complementary Features for Image Recognition,
ICCV17(502-510)
IEEE DOI
1802
2 parallel neural nets.
feature extraction, image classification, image representation,
learning (artificial intelligence), neural nets, Dual-Net, DualNet,
Visualization
BibRef
Eisenschtat, A.[Aviv],
Wolf, L.B.[Lior B.],
Linking Image and Text with 2-Way Nets,
CVPR17(1855-1865)
IEEE DOI
1711
Adaptation models, Computer architecture,
Correlation, Neurons, Training
BibRef
Valadez-Godínez, S.[Sergio],
González, J.[Javier],
Sossa, H.[Humberto],
Efficient Pattern Recognition Using the Frequency Response of a Spiking
Neuron,
MCPR17(53-62).
Springer DOI
1706
BibRef
Papandreou, G.[George],
Kokkinos, I.[Iasonas],
Savalle, P.A.[Pierre-Andre],
Modeling local and global deformations in Deep Learning: Epitomic
convolution, Multiple Instance Learning, and sliding window detection,
CVPR15(390-399)
IEEE DOI
1510
BibRef
Zhou, X.Z.[Xiang-Zeng],
Xie, L.[Lei],
Zhang, P.[Peng],
Zhang, Y.[Yanning],
An ensemble of deep neural networks for object tracking,
ICIP14(843-847)
IEEE DOI
1502
Boosting
BibRef
Miclut, B.[Bogdan],
Committees of Deep Feedforward Networks Trained with Few Data,
GCPR14(736-742).
Springer DOI
1411
BibRef
Yu, W.[Wei],
Yang, K.Y.[Kui-Yuan],
Bai, Y.L.[Ya-Long],
Yao, H.X.[Hong-Xun],
Rui, Y.[Yong],
DNN Flow: DNN Feature Pyramid based Image Matching,
BMVC14(xx-yy).
HTML Version.
1410
Deep Neural Network
BibRef
Agrawal, P.[Pulkit],
Girshick, R.[Ross],
Malik, J.[Jitendra],
Analyzing the Performance of Multilayer Neural Networks for Object
Recognition,
ECCV14(VII: 329-344).
Springer DOI
1408
BibRef
Chakraborty, D.[Debrup],
Neural Network Ensembles from Training Set Expansions,
CIARP09(629-636).
Springer DOI
0911
BibRef
Húsek, D.[Dušan],
Moravec, P.[Pavel],
Snášel, V.[Václav],
Frolov, A.[Alexander],
Rezanková, H.[Hana],
Polyakov, P.[Pavel],
Comparison of Neural Network Boolean Factor Analysis Method with Some
Other Dimension Reduction Methods on Bars Problem,
PReMI07(235-243).
Springer DOI
0712
BibRef
Sridharan, K.[Karthik],
Beal, M.J.[Matthew J.],
Govindaraju, V.[Venu],
Competitive Mixtures of Simple Neurons,
ICPR06(II: 494-497).
IEEE DOI
0609
BibRef
Lefebvre, G.[Gregoire],
Laurent, C.[Christophe],
Ros, J.[Julien],
Garcia, C.[Christophe],
Supervised Image Classification by SOM Activity Map Comparison,
ICPR06(II: 728-731).
IEEE DOI
0609
BibRef
Ros, J.[Julien],
Laurent, C.[Christophe],
Lefebvre, G.[Grégoire],
A Cascade of Unsupervised and Supervised Neural Networks for Natural
Image Classification,
CIVR06(92-101).
Springer DOI
0607
See also Bag of Strings Representation for Image Categorization, A.
BibRef
Qin, K.[Ke],
Oommen, B.J.[B. John],
Chaotic Pattern Recognition:
The Spectrum of Properties of the Adachi Neural Network,
SSPR08(540-550).
Springer DOI
0812
BibRef
Calitoiu, D.[Dragos],
Oommen, J.B.[John B.],
Nussbaum, D.[Doron],
Modeling Inaccurate Perception: Desynchronization Issues of a Chaotic
Pattern Recognition Neural Network,
SCIA05(821-830).
Springer DOI
0506
BibRef
Adeodato, P.J.L.,
Vasconcelos, G.C.,
Arnaud, A.L.,
Cunha, R.C.L.V.,
Monteiro, D.S.M.P.,
A systematic solution for the NN3 Forecasting Competition problem based
on an ensemble of MLP neural networks,
ICPR08(1-4).
IEEE DOI
0812
Multilayer Perceptron
BibRef
Adeodato, P.J.L.,
Vasconcelos, G.C.,
Arnaud, A.L.,
Santos, R.A.F.,
Cunha, R.C.L.V.,
Monteiro, D.S.M.P.,
Neural Networks vs. Logistic Regression:
A Comparative Study on a Large Data Set,
ICPR04(III: 355-358).
IEEE DOI
0409
BibRef
Steinkraus, D.,
Buck, I.,
Simard, P.Y.,
Using GPUs for machine learning algorithms,
ICDAR05(II: 1115-1120).
IEEE DOI
0508
BibRef
Doering, A.,
Witte, H.,
Feedforward Neural Networks for Bayes-Optimal Classification:
Investigations on the Influence of the Composition of the
Training Set on the Cost Function,
ICPR96(IV: 219-223).
IEEE DOI
9608
(Klinikum der Friedrich Schiller-Univ. D)
BibRef
Holt, M.J.J.,
Comparison of generalization in multi-layer perceptrons with the
log-likelihood and least-squares cost functions,
ICPR92(II:17-20).
IEEE DOI
9208
BibRef
Chen, C.H.,
A comparison of neural network models for pattern recognition,
ICPR90(II: 45-46).
IEEE DOI
9208
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Architecture, Neural Architecture Search .