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Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.
A. S. Kashiet al., “Nonlinear signal-to-noise ratio estimation in coherent optical fiber transmission systems using artificial neural networks,” J. Lightw. Technol., vol. 36, no. 23, pp. 5424–5431, 2018.
D. Wanget al., “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol. 29, no. 19, pp. 1667–1670, 2017.
S. Odaet al., “A learning living network with open ROADMs,” J. Lightw. Technol., vol. 35, no. 8, pp. 1350–1356, 2017.
D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452, 2016.
N. Srivastavaet al., “Dropout: A simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.
F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984, 2012.
T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667, 2010.
X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589, 2009.
T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736, 2009.
M. Abadiet al., “Tensorflow: A system for large-scale machine learning,” in Proc. 12th USENIX Symp. Oper. Syst. Des. Implementation, 2016, pp. 265–283.
Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.
T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736, 2009.
J. Ba and D. Kingma, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Represent., 2015.
J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011, pp. 315–323.
C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011, pp. 315–323.
T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736, 2009.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736, 2009.
T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667, 2010.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in Proc. 33rd Int. Conf. Mach. Learn., 2016, pp. 1651–1660.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in Proc. 33rd Int. Conf. Mach. Learn., 2016, pp. 1651–1660.
X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011, pp. 315–323.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736, 2009.
T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.
T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.
T. Tanimura, T. Kato, S. Watanabe, and T. Hoshida, “Deep neural network based optical monitor providing self-confidence as auxiliary output,” in Proc. Eur. Conf. Opt. Commun., 2018, Paper We1D.5.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. 32nd Int. Conf. Mach. Learn., 2015, pp. 448–456.
X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589, 2009.
D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452, 2016.
J. Wass, J. Thrane, M. Piels, R. Jones, and D. Zibar, “Gaussian process regression for WDM system performance prediction,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2017, Paper Tu3D.7.
A. S. Kashiet al., “Nonlinear signal-to-noise ratio estimation in coherent optical fiber transmission systems using artificial neural networks,” J. Lightw. Technol., vol. 36, no. 23, pp. 5424–5431, 2018.
T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.
T. Tanimura, T. Kato, S. Watanabe, and T. Hoshida, “Deep neural network based optical monitor providing self-confidence as auxiliary output,” in Proc. Eur. Conf. Opt. Commun., 2018, Paper We1D.5.
F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984, 2012.
J. Ba and D. Kingma, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Represent., 2015.
T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736, 2009.
F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984, 2012.
T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667, 2010.
J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.
T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736, 2009.
Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.
F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984, 2012.
F. Menget al., “Field trial of Gaussian process learning of function-agnostic channel performance under uncertainty,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper W4F.5.
T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667, 2010.
T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.
T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.
J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.
S. Odaet al., “A learning living network with open ROADMs,” J. Lightw. Technol., vol. 35, no. 8, pp. 1350–1356, 2017.
X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589, 2009.
J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.
D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452, 2016.
J. Wass, J. Thrane, M. Piels, R. Jones, and D. Zibar, “Gaussian process regression for WDM system performance prediction,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2017, Paper Tu3D.7.
C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). Cambridge, MA, USA: MIT Press, 2006.
T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.
D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452, 2016.
J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.
F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984, 2012.
T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667, 2010.
X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589, 2009.
J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.
Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.
N. Srivastavaet al., “Dropout: A simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.
T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. 32nd Int. Conf. Mach. Learn., 2015, pp. 448–456.
T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.
T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.
T. Tanimuraet al., “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in Proc. Eur. Conf. Opt. Commun., 2016, Paper Tu.2.C.2.
T. Tanimura, T. Kato, S. Watanabe, and T. Hoshida, “Deep neural network based optical monitor providing self-confidence as auxiliary output,” in Proc. Eur. Conf. Opt. Commun., 2018, Paper We1D.5.
J. Wass, J. Thrane, M. Piels, R. Jones, and D. Zibar, “Gaussian process regression for WDM system performance prediction,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2017, Paper Tu3D.7.
D. Wanget al., “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol. 29, no. 19, pp. 1667–1670, 2017.
J. Wass, J. Thrane, M. Piels, R. Jones, and D. Zibar, “Gaussian process regression for WDM system performance prediction,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2017, Paper Tu3D.7.
T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.
T. Tanimura, T. Kato, S. Watanabe, and T. Hoshida, “Deep neural network based optical monitor providing self-confidence as auxiliary output,” in Proc. Eur. Conf. Opt. Commun., 2018, Paper We1D.5.
C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). Cambridge, MA, USA: MIT Press, 2006.
X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589, 2009.
X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589, 2009.
S. Yanet al., “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in Proc. 43rd Eur. Conf. Opt. Commun., 2017, pp. 793–795.
F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984, 2012.
D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452, 2016.
J. Wass, J. Thrane, M. Piels, R. Jones, and D. Zibar, “Gaussian process regression for WDM system performance prediction,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2017, Paper Tu3D.7.
D. Wanget al., “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol. 29, no. 19, pp. 1667–1670, 2017.
T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667, 2010.
F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984, 2012.
X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589, 2009.
S. Odaet al., “A learning living network with open ROADMs,” J. Lightw. Technol., vol. 35, no. 8, pp. 1350–1356, 2017.
D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452, 2016.
T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736, 2009.
A. S. Kashiet al., “Nonlinear signal-to-noise ratio estimation in coherent optical fiber transmission systems using artificial neural networks,” J. Lightw. Technol., vol. 36, no. 23, pp. 5424–5431, 2018.
N. Srivastavaet al., “Dropout: A simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.
T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.
T. Tanimuraet al., “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in Proc. Eur. Conf. Opt. Commun., 2016, Paper Tu.2.C.2.
T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.
Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.
J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.
X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011, pp. 315–323.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. 32nd Int. Conf. Mach. Learn., 2015, pp. 448–456.
J. Ba and D. Kingma, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Represent., 2015.
M. Abadiet al., “Tensorflow: A system for large-scale machine learning,” in Proc. 12th USENIX Symp. Oper. Syst. Des. Implementation, 2016, pp. 265–283.
S. Yanet al., “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in Proc. 43rd Eur. Conf. Opt. Commun., 2017, pp. 793–795.
C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). Cambridge, MA, USA: MIT Press, 2006.
C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
J. Wass, J. Thrane, M. Piels, R. Jones, and D. Zibar, “Gaussian process regression for WDM system performance prediction,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2017, Paper Tu3D.7.
F. Menget al., “Field trial of Gaussian process learning of function-agnostic channel performance under uncertainty,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper W4F.5.
T. Tanimura, T. Kato, S. Watanabe, and T. Hoshida, “Deep neural network based optical monitor providing self-confidence as auxiliary output,” in Proc. Eur. Conf. Opt. Commun., 2018, Paper We1D.5.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in Proc. 33rd Int. Conf. Mach. Learn., 2016, pp. 1651–1660.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
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