Derin Öğrenme Modelleri ve Uygulama Alanlarına İlişkin Bir Derleme

Yıl 2019, Cilt: 10 Sayı: 2, 409 - 445, 20.06.2019

Ferdi Doğan İbrahim Türkoğlu

https://doi.org/10.24012/dumf.411130

Cited By: 17

Öz

Derin öğrenme makine öğreniminin bir koludur. Makine öğreniminin başlarından günümüze kadar geçen süreçte yapay zekaya olan ilgi giderek artmış ve günümüzde en çok kullanılan yapay zeka algoritmaları olan derin öğrenme mimarilerinin ortaya çıkmasını sağlamıştır. Derin öğrenme mimarileri ile birlikte yapay zeka problemlerinin çözümü için pek çok derin öğrenme yaklaşımları geliştirilmiştir. Endüstri, tıp, robotik, görüntü işleme, bilgisayar görmesi, nesne tespiti, ses işleme-tanıma, çeviri, gelecek tahmini, finansal gibi pek çok alanda akıllı çözümler üretmektedir. Bu çalışmada, derin öğrenme mimarileri ve algoritmaları incelenerek, literatürde yapılmış çalışmalar ışığında uygulama alanları temelinde başarımları değerlendirilmiştir. Derin öğrenme mimarileri ile birlikte derin öğrenmede kullanılan kütüphanelere yer verilmiştir. Bununla beraber farklı problemlerin çözümlerine yönelik geliştirilen derin öğrenme mimarileri yer almaktadır.

Anahtar Kelimeler

Derin öğrenme, Yapay sinir ağları, Derin CNN, Konvolüsyonel sinir ağları

Kaynakça

• Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
• Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Kudlur, M. (2016, November). TensorFlow: A System for Large-Scale Machine Learning. In OSDI (Vol. 16, pp. 265-283).
• Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1987). A learning algorithm for Boltzmann machines. In Readings in Computer Vision (pp. 522-533).
• Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., & Barkan, E. (2016). A region based convolutional network for tumor detection and classification in breast mammography. In Deep Learning and Data Labeling for Medical Applications (pp. 197-205). Springer, Cham.
• Al Rahhal, M. M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., & Yager, R. R. (2016). Deep learning approach for active classification of electrocardiogram signals. Information Sciences, 345, 340-354.
• Alexe, B., Deselaers, T., & Ferrari, V. (2012). Measuring the objectness of image windows. IEEE transactions on pattern analysis and machine intelligence, 34(11), 2189-2202.
• An, X., Kuang, D., Guo, X., Zhao, Y., & He, L. (2014, August). A deep learning method for classification of EEG data based on motor imagery. In International Conference on Intelligent Computing (pp. 203-210). Springer, Cham.
• An, X., Kuang, D., Guo, X., Zhao, Y., & He, L. (2014, August). A deep learning method for classification of EEG data based on motor imagery. In International Conference on Intelligent Computing (pp. 203-210). Springer, Cham.
• Angelova, A., Krizhevsky, A., & Vanhoucke, V. (2015, May). Pedestrian detection with a large-field-of-view deep network. In Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 704-711). IEEE.
• Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017). DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome biology, 18(1), 67.
• Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE transactions on medical imaging, 35(5), 1207-1216.
• Antony, J., McGuinness, K., O'Connor, N. E., & Moran, K. (2016, December). Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. In Pattern Recognition (ICPR), 2016 23rd International Conference on (pp. 1195-1200). IEEE.
• Asgari, E., & Mofrad, M. R. (2015). Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one, 10(11), e0141287.Assael, Y. M., Shillingford, B., Whiteson, S., & de Freitas, N. (2016). LipNet: end-to-end sentence-level lipreading.
• Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
• Baldi, P. (2012, June). Autoencoders, unsupervised learning, and deep architectures. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning (pp. 37-49).
• Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2018). Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification. arXiv preprint arXiv:1803.02315.
• Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., ... & Bengio, Y. (2012). Theano: new features and speed improvements. arXiv preprint arXiv:1211.5590.
• Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1), 1-127.
• Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Advances in neural information processing systems (pp. 153-160).
• Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
• Boureau, Y. L., & Cun, Y. L. (2008). Sparse feature learning for deep belief networks. In Advances in neural information processing systems (pp. 1185-1192).
• Broomhead, D. S., & Lowe, D. (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks (No. RSRE-MEMO-4148). Royal Signals and Radar Establishment Malvern (United Kingdom).
• Brueckner, R., & Schulter, B. (2014, May). Social signal classification using deep BLSTM recurrent neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on (pp. 4823-4827). IEEE.
• Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
• Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017, July). Realtime multi-person 2d pose estimation using part affinity fields. In CVPR (Vol. 1, No. 2, p. 7).
• Chamberlain, D., Kodgule, R., Ganelin, D., Miglani, V., & Fletcher, R. R. (2016, August). Application of semi-supervised deep learning to lung sound analysis. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 804-807). IEEE.
• Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015). PCANet: A simple deep learning baseline for image classification?. IEEE Transactions on Image Processing, 24(12), 5017-5032.
• Chapelle, O., Scholkopf, B., & Zien, A. (2009). Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]. IEEE Transactions on Neural Networks, 20(3), 542-542.
• Chen, C. L., Mahjoubfar, A., Tai, L. C., Blaby, I. K., Huang, A., Niazi, K. R., & Jalali, B. (2016). Deep learning in label-free cell classification. Scientific reports, 6, 21471.
• Chen, X., Xiang, S., Liu, C. L., & Pan, C. H. (2013, November). Aircraft detection by deep belief nets. In Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on (pp. 54-58). IEEE.
• Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014). Deep learning-based classification of hyperspectral data. IEEE Journal of Selected topics in applied earth observations and remote sensing, 7(6), 2094-2107.
• Cheng, G., & Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28.
• Cheng, G., Zhou, P., & Han, J. (2016). Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7405-7415.
• Cheng, M. M., Zhang, Z., Lin, W. Y., & Torr, P. (2014). BING: Binarized normed gradients for objectness estimation at 300fps. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3286-3293).
• Cheng, Z., Yang, Q., & Sheng, B. (2015). Deep colorization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 415-423).
• Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
• Chollet, F. (2016). Xception: Deep learning with depthwise separable convolutions. arXiv preprint.
• Cicero, M., Bilbily, A., Colak, E., Dowdell, T., Gray, B., Perampaladas, K., & Barfett, J. (2017). Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Investigative radiology, 52(5), 281-287.
• Ciregan, D., Meier, U., & Schmidhuber, J. (2012, June). Multi-column deep neural networks for image classification. In Computer vision and pattern recognition (CVPR), 2012 IEEE conference on (pp. 3642-3649). IEEE.
• Ciresan, D. C., Meier, U., Masci, J., Maria Gambardella, L., & Schmidhuber, J. (2011, July). Flexible, high performance convolutional neural networks for image classification. In IJCAI Proceedings-International Joint Conference on Artificial Intelligence (Vol. 22, No. 1, p. 1237).
• Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011). Torch7: A matlab-like environment for machine learning. In BigLearn, NIPS workshop (No. EPFL-CONF-192376).
• Cruz-Roa, A. A., Ovalle, J. E. A., Madabhushi, A., & Osorio, F. A. G. (2013, September). A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 403-410). Springer, Berlin, Heidelberg.
• Dahl, G. E., Stokes, J. W., Deng, L., & Yu, D. (2013, May). Large-scale malware classification using random projections and neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 3422-3426). IEEE.
• Dahl, R., Norouzi, M., & Shlens, J. (2017). Pixel recursive super resolution. arXiv preprint arXiv:1702.00783.de Brébisson, A., & Montana, G. (2015). Deep neural networks for anatomical brain segmentation. arXiv preprint arXiv:1502.02445.
• Diao, W., Sun, X., Zheng, X., Dou, F., Wang, H., & Fu, K. (2016). Efficient saliency-based object detection in remote sensing images using deep belief networks. IEEE Geoscience and Remote Sensing Letters, 13(2), 137-141.
• Dogan., F., Turkoglu, I., (2017). Classıfıcatıon Of Satellıte Images By Deep Learning. 8th International Advanved Teknologies Symposium.
• Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
• Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211.
• Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2014). Scalable object detection using deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2147-2154).
• Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115.
• Fakoor, R., Ladhak, F., Nazi, A., & Huber, M. (2013, June). Using deep learning to enhance cancer diagnosis and classification. In Proceedings of the International Conference on Machine Learning (Vol. 28).
• Fried, O., & Fiebrink, R. (2013). Cross-modal Sound Mapping Using Deep Learning. In NIME (pp. 531-534).
• Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., & Mikolov, T. (2013). Devise: A deep visual-semantic embedding model. In Advances in neural information processing systems(pp. 2121-2129).
• Fu, H., Xu, Y., Wong, D. W. K., & Liu, J. (2016, April). Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 698-701). IEEE.
• Fukushima, K. (1975). Cognitron: A self-organizing multilayered neural network. Biological cybernetics, 20(3-4), 121-136.
• Fukushima, K. (1986). A neural network model for selective attention in visual pattern recognition. Biological Cybernetics, 55(1), 5-15.
• Fukushima, K., & Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and cooperation in neural nets (pp. 267-285). Springer, Berlin, Heidelberg.
• Ganin, Y., Kononenko, D., Sungatullina, D., & Lempitsky, V. (2016, October). Deepwarp: Photorealistic image resynthesis for gaze manipulation. In European Conference on Computer Vision(pp. 311-326). Springer, Cham.
• Gao, Y., Hendricks, L. A., Kuchenbecker, K. J., & Darrell, T. (2016, May). Deep learning for tactile understanding from visual and haptic data. In Robotics and Automation (ICRA), 2016 IEEE International Conference on (pp. 536-543). IEEE.
• Gatys, L., Ecker, A. S., & Bethge, M. (2015). Texture synthesis using convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 262-270).
• Gers, F. A., & Schmidhuber, J. (2000). Recurrent nets that time and count. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Vol. 3, pp. 189-194). IEEE.
• Gers, F. A., Schmidhuber, J., & Cummins, F. (1999). Learning to forget: Continual prediction with LSTM.
• Girshick, R. (2015). Fast r-cnn. arXiv preprint arXiv:1504.08083.
• Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
• Glorot, X., Bordes, A., & Bengio, Y. (2011). Domain adaptation for large-scale sentiment classification: A deep learning approach. In Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 513-520).
• Glorot, X., Bordes, A., & Bengio, Y. (2011). Domain adaptation for large-scale sentiment classification: A deep learning approach. In Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 513-520).
• Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
• Golkov, V., Dosovitskiy, A., Sperl, J. I., Menzel, M. I., Czisch, M., Sämann, P., ... & Cremers, D. (2016). q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE transactions on medical imaging, 35(5), 1344-1351.
• Goodfellow, I. J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., ... & Bengio, Y. (2013). Pylearn2: a machine learning research library. arXiv preprint arXiv:1308.4214.
• Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In Acoustics, speech and signal processing (icassp), 2013 ieee international conference on (pp. 6645-6649). IEEE.
• Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Kim, R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.
• Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48.
• Han, S., Mao, H., & Dally, W. J. (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149.
• Hannun, A., Case, C., Casper, J., Catanzaro, B., Diamos, G., Elsen, E., ... & Ng, A. Y. (2014). Deep speech: Scaling up end-to-end speech recognition. arXiv preprint arXiv:1412.5567.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). Unsupervised learning. In The elements of statistical learning (pp. 485-585). Springer, New York, NY.
• Havaei, M., Guizard, N., Chapados, N., & Bengio, Y. (2016, October). HeMIS: Hetero-modal image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 469-477). Springer, Cham.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
• Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep learning in finance. arXiv preprint arXiv:1602.06561.
• Hebb, D. (1949). The organization of behavior john wiley & sons. New York.
• Hilleli, B., & El-Yaniv, R. (2016). Deep Learning of Robotic Tasks using Strong and Weak Human Supervision. arXiv preprint arXiv:1612.01086.
• Hinton, G. E. (2012). A practical guide to training restricted Boltzmann machines. In Neural networks: Tricks of the trade (pp. 599-619). Springer, Berlin, Heidelberg.
• Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507.
• Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length and Helmholtz free energy. In Advances in neural information processing systems (pp. 3-10).
• Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
• Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
• Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82-97.
• Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82-97.
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
• Holder, L. B., Haque, M. M., & Skinner, M. K. (2017). Machine learning for epigenetics and future medical applications. Epigenetics, 12(7), 505-514.
• Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
• Hu, F., Xia, G. S., Hu, J., & Zhang, L. (2015). Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11), 14680-14707.
• Hua, K. L., Hsu, C. H., Hidayati, S. C., Cheng, W. H., & Chen, Y. J. (2015). Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and therapy, 8.
• Huang, F. J., Boureau, Y. L., & LeCun, Y. (2007, June). Unsupervised learning of invariant feature hierarchies with applications to object recognition. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on (pp. 1-8). IEEE.
• Hubel, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology, 195(1), 215-243.
• Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.
• Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2016). Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Transactions on Graphics (TOG), 35(4), 110.
• İNİK, Ö., & ÜLKER, E.,(2017) Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.
• Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456).
• Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. arXiv preprint.
• Jafari, M. H., Nasr-Esfahani, E., Karimi, N., Soroushmehr, S. M., Samavi, S., & Najarian, K. (2016). Extraction of skin lesions from non-dermoscopic images using deep learning. arXiv preprint arXiv:1609.02374.
• Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31-44.
• Jarrett, K., Kavukcuoglu, K., & LeCun, Y. (2009, September). What is the best multi-stage architecture for object recognition?. In Computer Vision, 2009 IEEE 12th International Conference on(pp. 2146-2153). IEEE.
• Jean, S., Cho, K., Memisevic, R., & Bengio, Y. (2014). On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007.
• Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., ... & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 675-678). ACM.
• Jones, M. S. (2015). Convolutional autoencoders in python/theano/lasagne. Blog post (retrieved February 17, 2016), April.
• Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
• Kappen, H. J. (1994). Using boltzmann machines for probability estimation: A general framework for neural network learning. In Machine Intelligence and Pattern Recognition (Vol. 16, pp. 299-312). North-Holland.
• Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 3128-3137).
• Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 1725-1732).
• Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., ... & Hamarneh, G. (2017). BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 146, 1038-1049.
• Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
• Kochura, Y., Stirenko, S., Rojbi, A., Alienin, O., Novotarskiy, M., & Gordienko, Y. (2017). Comparative analysis of open source frameworks for machine learning with use case in single-threaded and multi-threaded modes. arXiv preprint arXiv:1706.02248.
• Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.
• Kreutzer, J., Schamoni, S., & Riezler, S. (2015). Quality estimation from scratch (quetch): Deep learning for word-level translation quality estimation. In Proceedings of the Tenth Workshop on Statistical Machine Translation (pp. 316-322).
• Krizhevsky, A., & Hinton, G. E. (2011, April). Using very deep autoencoders for content-based image retrieval. In ESANN.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
• Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782.
• Larsson, G., Maire, M., & Shakhnarovich, G. (2016, October). Learning representations for automatic colorization. In European Conference on Computer Vision (pp. 577-593). Springer, Cham.
• LeCun, Y., & Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10), 1995.
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
• Lee, H., Pham, P., Largman, Y., & Ng, A. Y. (2009). Unsupervised feature learning for audio classification using convolutional deep belief networks. In Advances in neural information processing systems (pp. 1096-1104).
• Lenz, I., Lee, H., & Saxena, A. (2015). Deep learning for detecting robotic grasps. The International Journal of Robotics Research, 34(4-5), 705-724.
• Levine, S., Pastor, P., Krizhevsky, A., & Quillen, D. (2016, October). Learning hand-eye coordination for robotic grasping with large-scale data collection. In International Symposium on Experimental Robotics (pp. 173-184). Springer, Cham.
• Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015). A convolutional neural network cascade for face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5325-5334).
• Li, J., Dai, W., Metze, F., Qu, S., & Das, S. (2017, March). A comparison of deep learning methods for environmental sound detection. In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on (pp. 126-130). IEEE.
• Li, T. L., Chan, A. B., & Chun, A. (2010, March). Automatic musical pattern feature extraction using convolutional neural network. In Proc. Int. Conf. Data Mining and Applications.
• Lin, M., Chen, Q., & Yan, S. (2013). Network in network. arXiv preprint arXiv:1312.4400.
• Lippmann, R. (1987). An introduction to computing with neural nets. IEEE Assp magazine, 4(2), 4-22.
• Liu, H., Li, L., & Ma, J. (2016). Rolling bearing fault diagnosis based on STFT-deep learning and sound signals. Shock and Vibration, 2016.
• Lu, X., Tsao, Y., Matsuda, S., & Hori, C. (2013, August). Speech enhancement based on deep denoising autoencoder. In Interspeech (pp. 436-440).
• Luus, F. P., Salmon, B. P., Van den Bergh, F., & Maharaj, B. T. J. (2015). Multiview deep learning for land-use classification. IEEE Geoscience and Remote Sensing Letters, 12(12), 2448-2452.
• Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2015). Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865-873.
• McClelland, J. L., Rumelhart, D. E., & Hinton, G. E. (1986). The appeal of parallel distributed processing. MIT Press, Cambridge MA, 3-44.
• McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
• Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.
• Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
• Minsky, M., Papert, S. A., & Bottou, L. (1969). Perceptrons: An introduction to computational geometry. MIT press.
• Morris, R. J., & Rubin, L. D. (1991). U.S. Patent No. 5,060,276. Washington, DC: U.S. Patent and Trademark Office.
• Murugappan, V., & Sabeenian, R. S. (2017). Texture based medical image classification by using multi-scale gabor rotation-invariant local binary pattern (MGRLBP). Cluster Computing, 1-14.
• Nair, A., Srinivasan, P., Blackwell, S., Alcicek, C., Fearon, R., De Maria, A., ... & Legg, S. (2015). Massively parallel methods for deep reinforcement learning. arXiv preprint arXiv:1507.04296.
• Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).
• Ng, J. Y. H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. (2015, June). Beyond short snippets: Deep networks for video classification. In Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on (pp. 4694-4702). IEEE.
• Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., & Yosinski, J. (2017, July). Plug & play generative networks: Conditional iterative generation of images in latent space. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on (pp. 3510-3520). IEEE.
• NVIDIA, 2016. NVIDIA deep learning gpu training system. https://developer.nvidia.com/digits. Erişim: 17.03.2018.
• Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., ... & Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499.
• Ouyang, W., & Wang, X. (2013, December). Joint deep learning for pedestrian detection. In Computer Vision (ICCV), 2013 IEEE International Conference on (pp. 2056-2063). IEEE.
• Owens, A., Isola, P., McDermott, J., Torralba, A., Adelson, E. H., & Freeman, W. T. (2016). Visually indicated sounds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2405-2413).
• Pan, Z., Rust, A. G., & Bolouri, H. (2000). Image redundancy reduction for neural network classification using discrete cosine transforms. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Vol. 3, pp. 149-154). IEEE.
• Pang, Y., Sun, M., Jiang, X., & Li, X. (2017). Convolution in convolution for network in network. IEEE transactions on neural networks and learning systems.
• Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015, September). Deep Face Recognition. In BMVC (Vol. 1, No. 3, p. 6).
• Piczak, K. J. (2015, September). Environmental sound classification with convolutional neural networks. In Machine Learning for Signal Processing (MLSP), 2015 IEEE 25th International Workshop on (pp. 1-6). IEEE.
• Plis, S. M., Hjelm, D. R., Salakhutdinov, R., Allen, E. A., Bockholt, H. J., Long, J. D., ... & Calhoun, V. D. (2014). Deep learning for neuroimaging: a validation study. Frontiers in neuroscience, 8, 229.
• Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 1(2), 4.
• Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.
• R. Girshick, J. Donahue, T. Darrell, et al., Rich feature hierarchies for accurate object detection and semantic segmentation, in: Proceedings of the CVPR, 2014.
• Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., & Pande, V. (2015). Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072.
• Ren, L., Cui, J., Sun, Y., & Cheng, X. (2017). Multi-bearing remaining useful life collaborative prediction: A deep learning approach. Journal of Manufacturing Systems, 43, 248-256.
• Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems(pp. 91-99).
• Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems(pp. 91-99).
• Robinson, J. A. (1965). A machine-oriented logic based on the resolution principle. Journal of the ACM (JACM), 12(1), 23-41.
• Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
• Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Berg, A. C. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252.
• Sajikumar, N., & Thandaveswara, B. S. (1999). A non-linear rainfall–runoff model using an artificial neural network. Journal of hydrology, 216(1-2), 32-55.
• Salamon, J., & Bello, J. P. (2017). Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Processing Letters, 24(3), 279-283.
• Sarle, W. S. (1994). Neural networks and statistical models.
• Sarraf, S., & Tofighi, G. (2016). Classification of alzheimer's disease using fmri data and deep learning convolutional neural networks. arXiv preprint arXiv:1603.08631.
• Scherer, D., Müller, A., & Behnke, S. (2010, September). Evaluation of pooling operations in convolutional architectures for object recognition. In International conference on artificial neural networks (pp. 92-101). Springer, Berlin, Heidelberg.
• Schmidhuber, J. (1992). Learning complex, extended sequences using the principle of history compression. Neural Computation, 4(2), 234-242.
• Schölkopf, B., Burges, C., & Vapnik, V. (1996, July). Incorporating invariances in support vector learning machines. In International Conference on Artificial Neural Networks (pp. 47-52). Springer, Berlin, Heidelberg.
• Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681.
• Sebastiani, F. (2002). Machine learning in automated text categorization. ACM computing surveys (CSUR), 34(1), 1-47.
• Şeker, A., Diri, B., & Balık, H. H. (2017). Derin Öğrenme Yöntemleri ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3), 47-64.
• Shang, C., Yang, F., Huang, D., & Lyu, W. (2014). Data-driven soft sensor development based on deep learning technique. Journal of Process Control, 24(3), 223-233.
• Shin, H. C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., & Summers, R. M. (2016). Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2497-2506).
• Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C., ... & Ray, T. S. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine, 8(1), 68.
• Simard, P. Y., Steinkraus, D., & Platt, J. C. (2003, August). Best practices for convolutional neural networks applied to visual document analysis. In ICDAR (Vol. 3, pp. 958-962).
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
• Sirinukunwattana, K., Raza, S. E. A., Tsang, Y. W., Snead, D. R., Cree, I. A., & Rajpoot, N. M. (2016). Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE transactions on medical imaging, 35(5), 1196-1206.
• Snoek, C. G., Worring, M., & Smeulders, A. W. (2005, November). Early versus late fusion in semantic video analysis. In Proceedings of the 13th annual ACM international conference on Multimedia (pp. 399-402). ACM.
• Sommer, R., & Paxson, V. (2010, May). Outside the closed world: On using machine learning for network intrusion detection. In Security and Privacy (SP), 2010 IEEE Symposium on (pp. 305-316). IEEE.
• Specht, D. F. (1988, July). Probabilistic neural networks for classification, mapping, or associative memory. In IEEE international conference on neural networks (Vol. 1, No. 24, pp. 525-532).
• Specht, D. F. (1991). A general regression neural network. IEEE transactions on neural networks, 2(6), 568-576.
• Spencer, M., Eickholt, J., & Cheng, J. (2015). A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM transactions on computational biology and bioinformatics, 12(1), 103-112.
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929-1958.
• Stein, J. Y. (1956). Bibliography. Digital Signal Processing: A Computer Science Perspective, 829-848.
• Suk, H. I., & Shen, D. (2013, September). Deep learning-based feature representation for AD/MCI classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 583-590). Springer, Berlin, Heidelberg.
• Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014). Deep learning face representation by joint identification-verification. In Advances in neural information processing systems (pp. 1988-1996).
• Sun, Y., Liang, D., Wang, X., & Tang, X. (2015). Deepid3: Face recognition with very deep neural networks. arXiv preprint arXiv:1502.00873.
• Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104-3112).
• Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104-3112).
• Suwajanakorn, S., Seitz, S. M., & Kemelmacher-Shlizerman, I. (2017). Synthesizing obama: learning lip sync from audio. ACM Transactions on Graphics (TOG), 36(4), 95.
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015, June). Going deeper with convolutions. Cvpr.
• Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 115, 124-135.
• Tamura, S. I., & Tateishi, M. (1997). Capabilities of a four-layered feedforward neural network: four layers versus three. IEEE Transactions on Neural Networks, 8(2), 251-255.
• Tang, D., Wei, F., Qin, B., Liu, T., & Zhou, M. (2014). Coooolll: A deep learning system for twitter sentiment classification. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (pp. 208-212).
• Tang, Y. (2013). Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239.
• Tarando, S. R., Fetita, C., Faccinetto, A., & Brillet, P. Y. (2016, March). Increasing CAD system efficacy for lung texture analysis using a convolutional network. In Medical Imaging 2016: Computer-Aided Diagnosis (Vol. 9785, p. 97850Q). International Society for Optics and Photonics.
• Team, D. J. D. (2016). Deeplearning4j: Open-source distributed deep learning for the jvm. Apache Software Foundation License, 2.
• Team, T. T. D., Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., ... & Belopolsky, A. (2016). Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688.
• Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision, 104(2), 154-171.
• Vakalopoulou, M., Karantzalos, K., Komodakis, N., & Paragios, N. (2015, July). Building detection in very high resolution multispectral data with deep learning features. In Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International (pp. 1873-1876). IEEE.
• van Grinsven, M. J., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sánchez, C. I. (2016). Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE transactions on medical imaging, 35(5), 1273-1284.
• Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. (2008, July). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning (pp. 1096-1103). ACM.
• Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11(Dec), 3371-3408.
• Vinyals, O., Kaiser, Ł., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. (2015). Grammar as a foreign language. In Advances in Neural Information Processing Systems (pp. 2773-2781).
• Wan, L., Zeiler, M., Zhang, S., Le Cun, Y., & Fergus, R. (2013, February). Regularization of neural networks using dropconnect. In International Conference on Machine Learning (pp. 1058-1066).
• Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavior science. Unpublished Doctoral Dissertation, Harvard University.
• Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., & Lai, L. (2015). Deep learning for drug-induced liver injury. Journal of chemical information and modeling, 55(10), 2085-2093.
• Yadav, N., Yadav, A., & Kumar, M. (2015). History of Neural Networks. In An Introduction to Neural Network Methods for Differential Equations (pp. 13-15). Springer, Dordrecht.
• Yang, S., Luo, P., Loy, C. C., & Tang, X. (2015). From facial parts responses to face detection: A deep learning approach. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3676-3684).
• Yoo, Y., Tang, L. W., Brosch, T., Li, D. K., Metz, L., Traboulsee, A., & Tam, R. (2016). Deep learning of brain lesion patterns for predicting future disease activity in patients with early symptoms of multiple sclerosis. In Deep Learning and Data Labeling for Medical Applications (pp. 86-94). Springer, Cham.
• You, Y., Zhang, Z., Hsieh, C. J., Demmel, J., & Keutzer, K. (2017). 100-epoch ImageNet training with alexnet in 24 minutes. ArXiv e-prints.
• Yu, D., Eversole, A., Seltzer, M., Yao, K., Huang, Z., Guenter, B., ... & Droppo, J. (2014). An introduction to computational networks and the computational network toolkit. Microsoft Technical Report MSR-TR-2014–112.
• Yu, J., Weng, K., Liang, G., & Xie, G. (2013, December). A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation. In Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on(pp. 1175-1180). IEEE.
• Zeiler, M. D., & Fergus, R. (2013). Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv:1301.3557.
• Zeiler, M. D., Ranzato, M., Monga, R., Mao, M., Yang, K., Le, Q. V., ... & Hinton, G. E. (2013, May). On rectified linear units for speech processing. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 3517-3521). IEEE.
• Zhang, F., Du, B., Zhang, L., & Xu, M. (2016). Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Transactions on Geoscience and Remote Sensing, 54(9), 5553-5563.
• Zhang, Q., Xiao, Y., Dai, W., Suo, J., Wang, C., Shi, J., & Zheng, H. (2016). Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics, 72, 150-157.
• Zhang, R., Isola, P., & Efros, A. A. (2016, October). Colorful image colorization. In European Conference on Computer Vision(pp. 649-666). Springer, Cham.
• Zhang, Y., Sohn, K., Villegas, R., Pan, G., & Lee, H. (2015). Improving object detection with deep convolutional networks via bayesian optimization and structured prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 249-258).
• Zhao, W., & Du, S. (2016). Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4544-4554.
• Zhihong, C., Hebin, Z., Yanbo, W., Binyan, L., & Yu, L. (2017, July). A vision-based robotic grasping system using deep learning for garbage sorting. In Control Conference (CCC), 2017 36th Chinese (pp. 11223-11226). IEEE.
• Zhu, Y., Urtasun, R., Salakhutdinov, R., & Fidler, S. (2015, June). segdeepm: Exploiting segmentation and context in deep neural networks for object detection. In Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on (pp. 4703-4711). IEEE.
• Zitnick, C. L., & Dollár, P. (2014, September). Edge boxes: Locating object proposals from edges. In European Conference on Computer Vision (pp. 391-405). Springer, Cham.
• Zou, Q., Ni, L., Zhang, T., & Wang, Q. (2015). Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 12(11), 2321-2325.