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In this paper we use convolutional neural networks to find the H\"older exponent of simulated sample paths of the rBergomi model, a recently proposed stock price model used in mathematical finance.
Hinton, G.E., Krizhevsky, A., Salakhutdinov, R., Srivastava, N. and Sutskever, I., Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research
1929
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Mandelbrot, B.B., and Van Ness, J.W., Fractional Brownian motions, fractional noises, and applications, SIAM Review
1968
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Back, A.D., Chung-Tsoi, A., Giles, C.L. and Lawrence, S., Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks
1997
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Biagini, F., Hu, Y., Øksendal, B. and Zhang, T., Stochastic Calculus for Fractional Brownian Motion and Applications
2008
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Hinton, G.E., Krizhevsky, A. and Sutskever, I., ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems
2012
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Abdel-Hamid, O., Deng, L. and Yu, D., Exploring Convolutional Neural Network Structures and Optimization Techniques for Speech Recognition. Interspeech
2013
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Deng, L., Hinton, G. and Kingsbury, B., New types of deep neural network learning for speech recognition and related applications: an overview, IEEE International Conference on Acoustics, Speech and Signal Processing
2013
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Chang, Y.C., Efficiently Implementing the Maximum Likelihood Estimator for Hurst Exponent, Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014
2014
Cited alongside, same era.
Chen, C., Kornhauser, A., Seff, A. and Xiao, J. DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving, IEEE International Conference on Computer Vision (ICCV)
2015
Cited alongside, same era.
Bayer, C., Friz, P.K. and Gatheral, J., Pricing under rough volatility. Quant. Finance
2016
Cited alongside, same era.
Bengio, Y., Courville, Y. and Goodfellow, I., Deep Learning
2016
Cited alongside, same era.
Iandola, F.N., Jin, P.H., Keutzer, K. and Wu, B. SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Jacquier, A., Pakkanen, M.S. and Stone, H., Pathwise Large Deviations for the rough Bergomi model, J. of App. Prob
2018
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Kinh Gian Do, R., Nishio, M., Togashi, K. and Yamashita, R., Convolutional neural networks: an overview and application in radiology, Insights into Imaging
2018
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2019
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2019
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2017
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2018
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Gatheral, J., Jaisson, T. and Rosenbaum, M., Volatility is rough. Quant. Finance
2018
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2019
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2019
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