Mobile QR Code QR CODE


Abadi M., et al. , 2016, Tensorflow: A System for Large-scale Machine Learning, in Proc. of OSDI'16, berkeley, ca, usa, pp. 265-283URL
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
Bastien F., et al. , 2012, Theano: New Features and Speed Improvements, CoRRURL
Al-Rfou R., et al. , 2016, Theano: A Python Framework for Fast Computation of Mathematical Expressions, CoRRURL
2015, KerasURL
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
Collobert R., Bengio S., Mariéthoz J., 2002, Torch: A Modular Machine Learning Software Library, Technical Report, IDIAPURL
2017, PyTorch., URL
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
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
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
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
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
Ravì D., et al. , 2016, Deep Learning for Health Informatics, IEEE Journal of Biomedical and Health Informatics, Vol. 21, No. 1, pp. 4-21DOI
Salerno V., Rabbeni G., 2018, An Extreme Learning Machine Approach to Effective Energy Disaggregation, Electronics, Vol. 7, No. 10, pp. 235DOI
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
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
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
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
Vasilache N., et al. , 2014, Fast Convolutional Nets with fbfft: A GPU Performance Evaluation, CoRRURL
Winograd S., 1980, Arithmetic Complexity of Computations, Society for Industrial and Applied MathematicsDOI
Lavin A., Gray S., 2016, Fast Algorithms for Convolutional Neural Networks, in Proc. of CVPR'16, Las Vegas, NV, USA, pp. 4013-4021DOI
Chetlur S., et al. , 2014, cuDNN: Efficient Primitives for Deep Learning, CoRRURL
Deng J., et al. , 2009, ImageNet: A Large-scale Hierarchical Image Database, in Proc. of CVPR'09, Miami, FL, USA, pp. 248-255DOI
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