Matemática oculta bajo el proceso de aprendizaje en redes neuronales convolucionales
Resumen
En la última década la inteligencia artificial ha transformado el mundo. El Big Data y grandes empresas de software impulsan a investigadores a crear nuevos algoritmos que superan la inteligencia humana con mayor rapidez y eficiencia. En el año 2012 las redes neuronales convolucionales (CNN) captaron la atención de investigadores en el tema del reconocimiento de imágenes; volviéndose populares y eficientes. Sin embargo, la falta de información de un proceso matemático minucioso y la tendencia de autores a describir este método como una caja negra, implicaron que una arquitectura, parámetros e hiperparámetros definidos generen resultados con un proceso interno desconocido. El presente estudio desarrolló un modelo matemático detallado de forward y backward propagation en una CNN para imágenes tridimensionales (a color), dotando a investigadores de herramientas solidas al momento de generar optimizaciones en el algoritmo. Además, los modelos se aplicaron a una arquitectura planteada que permitió reconocer las etapas del proceso, cantidad de parámetros de aprendizaje de la red y complejidad enfocada al gasto computacional. Se incluye tablas con funciones de costo, activación y optimización más utilizadas que permitan al lector formular su propio modelo dependiendo de la arquitectura, funciones e hiperparámetros seleccionados.
Descargas
Citas
Asghar, M. Z., Habib, A., Habib, A., Khan, A., Ali, R., & Khattak, A. (2019). Exploring deep neural networks for rumor detection. Journal of Ambient Intelligence and Humanized Computing, 4315–4333. https://doi.org/10.1007/s12652-019-01527-4
Baselli, G., Codari, M., & Sardanelli, F. (2020). Opening the black box of machine learning in radiology: Can the proximity of annotated cases be a way? European Radiology Experimental, 4(1), 30. https://doi.org/10.1186/s41747-020-00159-0
Berzal, F. (2018). Redes Neuronales & Deep Learning. In Redes Neuronales & Deep Learning (pp. 194–200).
Bottou, L. (2012). Stochastic Gradient Descent Tricks. In G. Montavon, G. B. Orr, & K.-R. Müller (Eds.), Neural Networks: Tricks of the Trade (Vol. 7700, pp. 421–436). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-35289-8_25
Buhrmester, V., Münch, D., & Arens, M. (2019). Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey. ArXiv:1911.12116 [Cs]. http://arxiv.org/abs/1911.12116
Dhillon, A., & Verma, G. K. (2020). Convolutional neural network: A review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 9(2), 85–112. https://doi.org/10.1007/s13748-019-00203-0
Dumoulin, V., & Visin, F. (2018). A guide to convolution arithmetic for deep learning. ArXiv:1603.07285 [Cs, Stat]. http://arxiv.org/abs/1603.07285
Ghosh, A., & Kandasamy, D. (2020). Interpretable Artificial Intelligence: Why and When. American Journal of Roentgenology, 214(5), 1137–1138. https://doi.org/10.2214/AJR.19.22145
Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep Sparse Rectifier Neural Networks. 15, 9.
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377. https://doi.org/10.1016/j.patcog.2017.10.013
Haykin, S. (2009). Neural Networks and Learning Machines. In Neural Networks and Learning Machines (Third, pp. 478–481). Pearson-Prentice Hall.
Janocha, K., & Czarnecki, W. M. (2017). On Loss Functions for Deep Neural Networks in Classification. ArXiv:1702.05659. http://arxiv.org/abs/1702.05659
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. ArXiv:1408.5882 [Cs]. http://arxiv.org/abs/1408.5882
Koushik, J. (2016). Understanding Convolutional Neural Networks. ArXiv:1605.09081 [Stat]. http://arxiv.org/abs/1605.09081
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
Kullback, S., & Leibler, R. A. (1951). On Information and Sufficiency. Annals of Mathematical Statistics, 22(1), 79–86. https://doi.org/10.1214/aoms/1177729694
Kuo, C.-C. J. (2016). Understanding convolutional neural networks with a mathematical model. Journal of Visual Communication and Image Representation, 41, 406–413. https://doi.org/10.1016/j.jvcir.2016.11.003
Lazzeri, F. (2020, May 7). Selección de un algoritmo de Machine Learning. Azzure Machine Learning. https://docs.microsoft.com/es-es/azure/machine-learning/how-to-select-algorithms
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09, 1–8. https://doi.org/10.1145/1553374.1553453
Liu, T., Fang, S., Zhao, Y., Wang, P., & Zhang, J. (2015). Implementation of Training Convolutional Neural Networks. ArXiv:1506.01195-Computer Vision and Pattern Recognition, 10. arXiv:1506.01195.
Lopez-Pinaya, W., Vieira, S., Garcia-Dias, R., & Mechelli, A. (2020). Chapter 10—Convolutional neural networks. In A. Mechelli & S. Vieira (Eds.), Machine Learning (pp. 173–191). Academic Press. https://doi.org/10.1016/B978-0-12-815739-8.00010-9
Martineau, M., Raveaux, R., Conte, D., & Venturini, G. (2020). A Convolutional Neural Network into graph space. ArXiv:2002.09285 [Cs]. http://arxiv.org/abs/2002.09285
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2020). Deep Learning Based Text Classification: A Comprehensive Review. ArXiv:2004.03705. http://arxiv.org/abs/2004.03705
Namikawa, K., Hirasawa, T., Yoshio, T., Fujisaki, J., Ozawa, T., Ishihara, S., Aoki, T., Yamada, A., Koike, K., Suzuki, H., & Tada, T. (2020). Utilizing artificial intelligence in endoscopy: A clinician’s guide. Expert Review of Gastroenterology & Hepatology, 1–18. https://doi.org/10.1080/17474124.2020.1779058
Parmar, R. (2018, September 2). Common Loss functions in machine learning. Medium. https://towardsdatascience.com/common-loss-functions-in-machine-learning-46af0ffc4d23
Perconti, P., & Plebe, A. (2020). Deep learning and cognitive science. Cognition, 203, 104365. https://doi.org/10.1016/j.cognition.2020.104365
Pesapane, F., Tantrige, P., Patella, F., Biondetti, P., Nicosia, L., Ianniello, A., Rossi, U. G., Carrafiello, G., & Ierardi, A. M. (2020). Myths and facts about artificial intelligence: Why machine- and deep-learning will not replace interventional radiologists. Medical Oncology, 37(5), 40. https://doi.org/10.1007/s12032-020-01368-8
Rao, G. A., Syamala, K., Kishore, P. V. V., & Sastry, A. S. C. S. (2018). Deep convolutional neural networks for sign language recognition. 2018 Conference on Signal Processing And Communication Engineering Systems (SPACES), 194–197. https://doi.org/10.1109/SPACES.2018.8316344
Rawat, W., & Wang, Z. (2017). Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review. Neural Computation, 29(9), 2352–2449. https://doi.org/10.1162/neco_a_00990
Ruder, S. (2017). An overview of gradient descent optimization algorithms. ArXiv:1609.04747 [Cs]. http://arxiv.org/abs/1609.04747
Sturm, I., Lapuschkin, S., Samek, W., & Müller, K.-R. (2016). Interpretable deep neural networks for single-trial EEG classification. Journal of Neuroscience Methods, 274, 141–145. https://doi.org/10.1016/j.jneumeth.2016.10.008
Van Houdt, G., Mosquera, C., & Napoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review. https://doi.org/10.1007/s10462-020-09838-1
Wadawadagi, R., & Pagi, V. (2020). Sentiment analysis with deep neural networks: Comparative study and performance assessment. Artificial Intelligence Review. https://doi.org/10.1007/s10462-020-09845-2
Wu, J. (2017). Introduction to Convolutional Neural Networks. 2–28. https://cs.nju.edu.cn/wujx/paper/CNN.pdf
Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1253
Derechos de autor 2022 Ricardo Borja Robalino;Antonio Monleón Getino; José Rodellar
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
.jpg)
