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Deep neural networks continue to show improved performance with increasing depth, an encouraging trend that implies an explosion in the possible permutations of network architectures and hyperparameters for which there is little intuitive guidance.
Y. T. Zhou and R. Chellappa, “Computation of optical flow using a neural network,” in IEEE 1988 International Conference on Neural Networks , pp. 71–78 vol.2., 1988
1988
Earlier work this paper cites.
G. F. Miller, P. M. Todd, and S. U. Hegde, “Designing neural networks using genetic algorithms,” in Proceedings of the 3rd International Conference on Genetic Algorithms . San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., pp. 379–384., 1989
1989
Earlier work this paper cites.
D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning , 1st ed. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 1989
1989
Earlier work this paper cites.
Y. LeCun et al. , “Generalization and network design strategies,” Connectionism in perspective , pp. 143–155, 1989
1989
Earlier work this paper cites.
B. T. Zhang and H. Mühlenbein, “Balancing accuracy and parsimony in genetic programming,” Evolutionary Computation , vol. 3, no. 1, pp. 17–38, 1995
1995
Earlier work this paper cites.
T. Blickle and L. Thiele, “A comparison of selection schemes used in evolutionary algorithms,” Evolutionary Computation , vol. 4, no. 4, pp. 361–394, 1996
1996
Earlier work this paper cites.
Y. LeCun et al. ,“Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
J. Arifovic and R. Gençay, “Using genetic algorithms to select architecture of a feedforward artificial neural network,” Physica A: Statistical Mechanics and its Applications , vol. 289, no. 3, pp. 574 – 594, 2001
2001
Earlier work this paper cites.
J. Zhong et al. , “Comparison of performance between different selection strategies on simple genetic algorithms,” in International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06) , vol. 2, pp. 1115–1121., 2005
2005
Earlier work this paper cites.
R. Poli et al. , A Field Guide to Genetic Programming . Lulu Enterprises, UK Ltd, 2008
2008
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
A. L. Maas et al. , “Learning word vectors for sentiment analysis,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1 , ser. HLT ’11. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 142–150., 2011
2011
Cited alongside, same era.
A. L. Maas et al. , “Learning word vectors for sentiment analysis,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies . Portland, Oregon, USA: Association for Computational Linguistics, pp. 142–150., 2011
2011
Cited alongside, same era.
G. I. Sher, Handbook of Neuroevolution Through Erlang . Springer Publishing Company, Incorporated, 2012
2012
Cited alongside, same era.
2013
Cited alongside, same era.
F. Chollet et al. , “Keras,” https://github.com/fchollet/keras
2015
Later among the works it cites.
2016
Later among the works it cites.
M. A. J. Idrissi et al. , “Genetic algorithm for neural network architecture optimization,” in 2016 3rd International Conference on Logistics Operations Management (GOL) , pp. 1–4., 2016
2016
Later among the works it cites.
D. Vishwanath and S. Gupta, “Adding cnns to the mix: Stacking models for sentiment classification,” in 2016 IEEE Annual India Conference (INDICON) , pp. 1–4., 2016
2016
Later among the works it cites.
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L. Wan et al. ,“Regularization of neural networks using dropconnect,” in Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 , ser. ICML’13. JMLR.org, pp. III–1058–III–1066., 2013
2013
Cited alongside, same era.
2014
Cited alongside, same era.
N. Srivastava et al. , “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
R. K. Srivastava, K. Greff, and J. Schmidhuber, “Training very deep networks,” in Proceedings of the 28th International Conference on Neural Information Processing Systems , ser. NIPS’15. Cambridge, MA, USA: MIT Press, 2015, pp. 2377–2385. [Online]. Available: http://dl.acm.org/citation.cfm?id=2969442.2969505
2015
Cited alongside, same era.
M., Abadi et al. , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: http://tensorflow.org/
2015
Cited alongside, same era.
2017
Closest in time.
R. Miikkulainen et al. , “Evolving deep neural networks,” arXiv preprint arXiv:1703.00548 , 2017
2017
Closest in time.
2017
Closest in time.
E. Dufourq and B. A. Bassett, “Automated problem identification: Regression vs classification via evolutionary deep networks,” in Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists , ACM, 2017
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.