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REFERENCES

1 
Abadi M., et al. , 2016, Tensorflow: A System for Large-scale Machine Learning, in Proc. of OSDI'16, berkeley, ca, usa, pp. 265-283URL
2 
Jia Y., et al. , 2014, Caffe: Convolutional Architecture for Fast Feature Embedding, in Proc. of MM'14, new york, ny, usa, acm, pp. 675-678DOI
3 
Bastien F., et al. , 2012, Theano: New Features and Speed Improvements, CoRRURL
4 
Al-Rfou R., et al. , 2016, Theano: A Python Framework for Fast Computation of Mathematical Expressions, CoRRURL
5 
2015, KerasURL
6 
Seide F., Agarwal A., 2016, CNTK: Microsoft's Open-source Deep-learning Toolkit, in Proc. of KDD'16, New York, NY, USA, ACM, pp. 2135-2135DOI
7 
Collobert R., Bengio S., Mariéthoz J., 2002, Torch: A Modular Machine Learning Software Library, Technical Report, IDIAPURL
8 
2017, PyTorch., URL
9 
Salazar N. D., et al. , 2018, Application of Transfer Learning for Object Recognition Using Convolutional Neural Networks, in Proc. of 2018 IEEE Colombian Conference on Applications in Computational Intelligence, Medellin, Colombia, pp. 14-25DOI
10 
Spanhol F. A., et al. , 2016, Breast Cancer Histopathological Image Classification Using Convolutional Neural Networks, in Proc. of IJCNN'16, Vancouver, BC, Canada, pp. 2560-2567DOI
11 
Siniscalchi S. M., Salerno V. M., 2016, Adaptation to New Microphones Using Artificial Neural Networks with Trainable Activation Functions, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 8, pp. 1959-1965DOI
12 
Hautamäki V., et al. , 2015, Boosting Universal Speech Attributes Classification with Deep Neural Network for Foreign Accent Characterization, in Proc. of INTERSPEECH'15, Dresden, Germany, pp. 408-412URL
13 
Garzón-Alfonso C. C., Rodríguez-Martínez M., 2018, Twitter Health Surveillance (THS) System, in Proc. of 2018 IEEE International Conference on Big Data, Seattle, W,A USA, pp. 1647-1654DOI
14 
Ravì D., et al. , 2016, Deep Learning for Health Informatics, IEEE Journal of Biomedical and Health Informatics, Vol. 21, No. 1, pp. 4-21DOI
15 
Salerno V., Rabbeni G., 2018, An Extreme Learning Machine Approach to Effective Energy Disaggregation, Electronics, Vol. 7, No. 10, pp. 235DOI
16 
Zhang C., et al. , 2018, Sequence-to-point Learning with Neural Networks for Non-intrusive Load Monitoring, in Proc. of AAAI'18, New Orleans, LA, USA, pp. 2604-2611URL
17 
He K., Zhang X., Ren S., Sun J., 2015, Delving Deep into Rectifiers: Surpassing Human-level Performance on Imagenet Classification, in Proc. of ICCV'15, Santiago, Chile, pp. 1026-1034DOI
18 
Dodge S. F., Karam L. J., 2017, A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions, in Proc. of ICCCN'17, Vancouver, BC, Canada, pp. 1-7DOI
19 
He K., Zhang X., Ren S., Sun J., 2016, Deep Residual Learning for Image Recognition, in Proc. of CVPR'16, Las Vegas, NV, USA, pp. 770-778DOI
20 
Vasilache N., et al. , 2014, Fast Convolutional Nets with fbfft: A GPU Performance Evaluation, CoRRURL
21 
Winograd S., 1980, Arithmetic Complexity of Computations, Society for Industrial and Applied MathematicsDOI
22 
Lavin A., Gray S., 2016, Fast Algorithms for Convolutional Neural Networks, in Proc. of CVPR'16, Las Vegas, NV, USA, pp. 4013-4021DOI
23 
Chetlur S., et al. , 2014, cuDNN: Efficient Primitives for Deep Learning, CoRRURL
24 
Deng J., et al. , 2009, ImageNet: A Large-scale Hierarchical Image Database, in Proc. of CVPR'09, Miami, FL, USA, pp. 248-255DOI
25 
Rhu M., et al. , 2016, vDNN: Virtualized Deep Neural Networks for Scalable, Memory-efficient Neural Network Design, in Proc. of MICRO'16, Taipei, Taiwan, pp. 1-13DOI