Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network

P. F. Schmidt, M. A. Kraaijveld, and R. P. W. Duin, “Feed forward neural networks with random weights,” in Proc. 11th IAPR Int. Conf. on Pattern Recognition, Volume II, Conf. B: Pattern Recognition Methodology and Systems (ICPR11, The Hague, Aug.30 - Sep.3), IEEE Computer Society Press, Los Alamitos, CA, 1992, 1-4 , 1992

1992

Earlier work this paper cites.

C. L. P. Chen, “A rapid supervised learning neural network for function interpolation and approximation,” IEEE Transactions on Neural Networks , vol. 7, pp. 1220–1230, 1996

1996

Earlier work this paper cites.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, pp. 2278–2324, 1998

1998

Earlier work this paper cites.

C. Eliasmith and C. H. Anderson, Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems . MIT Press, Cambridge, MA, 2003

2003

Earlier work this paper cites.

P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” in Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003) , 2003

2003

Earlier work this paper cites.

G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks,” in In Proc. International Joint Conference on Neural Networks (IJCNN’2004), (Budapest, Hungary), July 25-29 , 2004

2004

Earlier work this paper cites.

Y. LeCun, F. J. Huang, and L. Bottou, “Learning methods for generic object recognition with invariance to pose and lighting,” in Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) , vol. 2, 2004, pp. 97–104

2004

Earlier work this paper cites.

A. Krizhevsky, “Learning multiple layers of features from tiny images,” Master’s thesis, Dept of CS, University of Toronto. See http://www.cs.toronto.edu/ kriz/cifar.html

2009

Earlier work this paper cites.

Q. V. Le, J. Ngiam, Z. Chen, D. Chia, P. W. Koh, and A. Y. Ng, “Tiled convolutional neural networks,” in Advances in Neural Information Processing Systems 23 , J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel, and A. Culotta, Eds., 2010, pp. 1279–1287

2010

Earlier work this paper cites.

D. Cireşan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Deep, big, simple neural nets for handwritten digit recognition,” Neural Computation , vol. 22, pp. 3207–3220, 2010

2010

Earlier work this paper cites.

A. Coates, H. Lee, and A. Y. Ng, “An analysis of single-layer networks in unsupervised feature learning,” in Proc.14th International Conference on Artificial Intelligence and Statistics (AISTATS), 2011, Fort Lauderdale, FL, USA. Volume 15 of JMLR:W&CP 15 , 2011

2011

Earlier work this paper cites.

A. Coates and A. Y. Ng, “The importance of encoding versus training with sparse coding and vector quantization,” in Proceedings of the 28th International Conference on Machine Learning (ICML), Bellevue, WA, USA , 2011

2011

Earlier work this paper cites.

Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” 2011, nIPS Workshop on Deep Learning and Unsupervised Feature Learning. See http://ufldl.stanford.edu/housenumbers

2011

Cited alongside, same era.

D. C. D. Cireşan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, high performance convolutional neural networks for image classification,” in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence , 2011, pp. 1237–1242

2011

Cited alongside, same era.

C. Eliasmith, T. C. Stewart, X. Choo, T. Bekolay, T. DeWolf, C. Tang, and D. Rasmussen, “A large-scale model of the functioning brain,” Science , vol. 338, pp. 1202–1205, 2012

2012

Cited alongside, same era.

G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics , vol. 42, pp. 513–529, 2012

W. Zhu, J. Miao, and L. Qing, “Constrained extreme learning machine: a novel highly discriminative random feedforward neural network,” in Proc. IJCNN , 2014, p. XXX

2014

Later among the works it cites.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” in International Conference on Learning Representations (ICLR 2014) . CBLS, 2014

2014

Later among the works it cites.

P. Sermanet, “A deep learning pipeline for image understanding and acoustic modeling,” Ph.D. dissertation, Department of Computer Science, New York University., 2014

2014

Later among the works it cites.

J. Mairal, P. Koniusz, Z. Harchaoui, and C. Schmid, “Convolutional kernel networks,” in Advances in Neural Information Processing Systems , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, Eds., vol. 27, 2014, pp. 2627–2635

2014

Later among the works it cites.