Evolutionary convolutional neural networks: An application to handwriting recognition

Neurocomputing - Tập 283 - Trang 38-52 - 2018
Alejandro Baldominos1, Yago Saez1, Pedro Isasi1
1Computer Science Department, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, Leganes 28911, Madrid

Tài liệu tham khảo

Yao, 1997, A new evolutionary system for evolving artificial neural networks, IEEE Trans. Neural Netw., 8, 694, 10.1109/72.572107 Stanley, 2002, Evolving neural networks through augmenting topologies, Evol. Comput., 10, 99, 10.1162/106365602320169811 Kassahun, 2005, Efficient reinforcement learning through evolutionary acquisition of neural topologies, 259 Koutník, 2014, Evolving deep unsupervised convolutional networks for vision-based reinforcement learning, 541 Verbancsics, 2015, Image classification using generative neuroevolution for deep learning, 488 Stanley, 2009, A hypercube-based encoding for evolving large-scale neural networks, Artif. Life, 15, 185, 10.1162/artl.2009.15.2.15202 Young, 2015, Optimizing deep learning hyper-parameters through an evolutionary algorithm Loshchilov, 2016, CMA-ES for hyperparameter optimization of deep neural networks, Fernando, 2016, Convolution by evolution: differentiable pattern producing networks, 109 B. Baker, 2017, Designing neural network architectures using reinforcement learning Zoph, 2017, Neural architecture search with reinforcement learning, arXiv, abs/1611.01578 Yu, 2017, iPrivacy: image privacy protection by identifying sensitive objects via deep multi-task learning, IEEE Trans. Inf. Forensics Secur., 12, 1005, 10.1109/TIFS.2016.2636090 Xie, 2017, Genetic CNN, arXiv, abs/1703.01513 Miikkulainen, 2017, Evolving deep neural networks, arXiv, abs/1703.00548 Desell, 2017, Large scale evolution of convolutional neural networks using volunteer computing, arXiv, abs/1703.05422 J. Davison, DEvol: automated deep neural network design via genetic programming, 2017, https://www.github.com/joeddav/devol; last visited on 2017-07-01. Suganuma, 2017, A genetic programming approach to designing convolutional neural network architectures, arXiv, abs/1704.00764 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Ian Goodfellow, 2017 LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 LeCun, 1998, Convolutional networks for images, speech, and time series, 255 Guo, 2016, Deep learning for visual understanding: a review, Neurocomputing, 187, 27, 10.1016/j.neucom.2015.09.116 Tsironi, 2017, An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition, Neurocomputing, 268, 76, 10.1016/j.neucom.2016.12.088 nez, 2016, Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition, Sensors, 16, 115, 10.3390/s16010115 Szegedy, 2015, Going deeper with convolutions, 1 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Cho, 2014, On the properties of neural machine translation: encoder-decoder approaches, arXiv, abs/1409.1259 Greff, 2016, LSTM: a search space odyssey, IEEE Trans. Neural Netw. Learn. Syst., PP Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929 Duchi, 2011, Adaptive subgradient methods for online learning and stochastic optimization, J. Mach. Learn. Res., 12, 2121 Zeiler, 2012, ADADELTA: an adaptive learning rate method, arXiv, abs/1212.5701 T. Tieleman, G. Hinton, Neural networks for machine learning, lecture 6.5 – RMSProp, 2012, Coursera, video available in http://www.youtube.com/watch?v=O3sxAc4hxZU. Kingma, 2014, Adam: a method for stochastic optimization, arXiv, abs/1412.6980 Holland, 1975 Ryan, 1998, Grammatical evolution: evolving programs for an arbitrary language, 1391, 83 Zhang, 2016, Hybrid orthogonal projection and estimation (HOPE): a new framework to learn neural networks, J. Mach. Learn. Res., 17, 1 Deng, 2011, Deep convex net: a scalable architecture for speech pattern classification, 2285 Lee, 2009, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, 609 Yang, 2010, Supervised translation-invariant sparse coding, 3517 Goodfellow, 2013, Multi-prediction deep Boltzmann machines, 548 Min, 2009, A deep non-linear feature mapping for large-margin kNN classification, arXiv, abs/0906.1814 Salakhutdinov, 2009, Deep Boltzmann machines, 5, 448 Chang, 2015, Batch-normalized maxout network in network, arXiv, abs/1511.02583 Lee, 2015, Generalizing pooling functions in convolutional neural networks: mixed, gated, and tree, 51, 464 Alom, 2017, Inception recurrent convolutional neural network for object recognition, arXiv, abs/1704.07709 Liang, 2015, Recurrent convolutional neural network for object recognition, 3367 Liao, 2015, On the importance of normalisation layers in deep learning with piecewise linear activation units, arXiv, abs/1508.00330 Hertel, 2015, Deep convolutional neural networks as generic feature extractors Graham, 2015, Fractional max-pooling, arXiv, abs/1412.6071 Liao, 2015, Competitive multi-scale convolution, arXiv, abs/1511.05635 McFonnell, 2015, Enhanced image classification with a fast-learning shallow convolutional neural network Mishkin, 2016, All you need is a good init Lee, 2015, Deeply-supervised nets, Vol. 38, 562 Mairal, 2014, Convolutional kernel networks, 2627 Xu, 2015, Multi-loss regularized deep neural network, IEEE Trans. Circuits Systems Video Technol., 26, 2273, 10.1109/TCSVT.2015.2477937 K. Jarrett, 2009, What is the best multi-stage architecture for object recognition?, 2146 Srivastava, 2015, Training very deep networks, 2377 Lin, 2014, Network in network Zeiler, 2013, Stochastic pooling for regularization of deep convolutional neural networks, arXiv, abs/1301.3557 Wan, 2013, Regularization of neural networks using DropConnect, Vol. 28 Labusch, 2008, Simple method for high-performance digit recognition based on sparse coding, IEEE Trans. Neural Netw., 19, 1985, 10.1109/TNN.2008.2005830 Ranzato, 2006, Efficient learning of sparse representations with an energy-based model, 1137 Ranzato, 2007, Unsupervised learning of invariant feature hierarchies with applications to object recognition Calderón, 2003, Handwritten digit recognition using convolutional neural networks and Gabor filters Le, 2011, On optimization methods for deep learning Yang, 2015, Deep fried convnets Lauer, 2007, A trainable feature extractor for handwritten digit recognition, Pattern Recogn., 40, 1816, 10.1016/j.patcog.2006.10.011 McFonnell, 2015, Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the ‘extreme learning machine’ algorithm, PLoS ONE, 10 Real, 2017, Large-scale evolution of image classifiers, arXiv, abs/1703.01041 Qian, 2018, Adaptive activation functions in convolutional neural networks, Neurocomputing, 272, 204, 10.1016/j.neucom.2017.06.070 Yang, 2017, The Euclidean embedding learning based on convolutional neural network for stereo matching, Neurocomputing, 267, 195, 10.1016/j.neucom.2017.06.007 Li, 2018, Training deep neural networks with discrete state transition, Neurocomputing, 272, 154, 10.1016/j.neucom.2017.06.058 Yu, 2017, Deep multimodal distance metric learning using click constraints for image ranking, IEEE Trans. Cybern., 47, 4014, 10.1109/TCYB.2016.2591583