RonRichman/gist:655cca0dd79afcd20b33d3131c537414

## gistfile1.txt
Bahdanau, D., K. Cho and Y. Bengio. 2014. "Neural machine translation by jointly learning to align and translate", arXiv preprint arXiv:1409.0473
Bengio, Y. 2009. "Learning deep architectures for AI", Foundations and trends® in Machine Learning 2(1):1-127.
Bengio, Y. 2012. "Deep learning of representations for unsupervised and transfer learning," Paper presented at Proceedings of ICML Workshop on Unsupervised and Transfer Learning. 17-36.
Bengio, Y., A. Courville and P. Vincent. 2013. "Representation learning: A review and new perspectives", IEEE transactions on pattern analysis and machine intelligence 35(8):1798-1828.
Brass, W. 1964. "Uses of census or survey data for the estimation of vital rates", Paper presented at African Seminar on Vital Statistics. Addis Ababa, 14-19 December 1964. United Nations.
Breeden, J.L. and E. Leonova. 2019. "When Big Data Isn’t Enough: Solving the long-range forecasting problem in supervised learning," Paper presented at 2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques (SMONT 2019). Atlantis Press.
Breiman, L. 2001. "Statistical modeling: The two cultures (with comments and a rejoinder by the author)", Statistical Science 16(3):199-231.
Castellani, G., U. Fiore, Z. Marino, L. Passalacqua et al. 2018. "An investigation of Machine Learning Approaches in the Solvency II Valuation Framework", Available at SSRN 3303296
Chung, J., C. Gulcehre, K. Cho and Y. Bengio. 2015. "Gated feedback recurrent neural networks," Paper presented at International Conference on Machine Learning. 2067-2075.
De Brébisson, A., É. Simon, A. Auvolat, P. Vincent et al. 2015. "Artificial neural networks applied to taxi destination prediction", arXiv arXiv:1508.00021
Donahue, J., P. Krähenbühl and T. Darrell. 2016. "Adversarial feature learning", arXiv preprint arXiv:1605.09782
Dong, W., J. Li, R. Yao, C. Li et al. 2016. "Characterizing driving styles with deep learning", arXiv arXiv:1607.03611
Dong, W., T. Yuan, K. Yang, C. Li et al. 2017. "Autoencoder regularized network for driving style representation learning", arXiv arXiv:1701.01272
Doyle, D. and C. Groendyke. 2018. "Using Neural Networks to Price and Hedge Variable Annuity Guarantees", Risks 7(1):1.
Ezzini, S., I. Berrada and M. Ghogho. 2018. "Who is behind the wheel? Driver identification and fingerprinting", Journal of Big Data 5(1):9.
Ferrario, A., A. Noll and M.V. Wuthrich. 2018. "Insights from Inside Neural Networks",
Friedman, J., T. Hastie and R. Tibshirani. 2009. The Elements of Statistical Learning : Data Mining, Inference, and Prediction. New York: Springer-Verlag. http://dx.doi.org/10.1007/978-0-387-84858-7.
Gabrielli, A. 2019. "A Neural Network Boosted Double Over-Dispersed Poisson Claims Reserving Model", Available at SSRN: https://ssrn.com/abstract=3365517
Gabrielli, A., R. Richman and M.V. Wuthrich. 2018. "Neural network embedding of the over-dispersed Poisson reserving model", Available at SSRN: https://ssrn.com/abstract=3288454
Gabrielli, A. and M. Wüthrich. 2018. "An Individual Claims History Simulation Machine", Risks 6(2):29.
Gal, Y. 2016. "Uncertainty in deep learning." Unpublished thesis, PhD thesis, University of Cambridge.
Gan, G. and X. Lin. 2015. "Valuation of large variable annuity portfolios under nested simulation: A functional data approach", Insurance: Mathematics and Economics 62:138-150.
Gao, G., S. Meng and M. Wüthrich. 2018. Claims Frequency Modeling Using Telematics Car Driving Data. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3102371. Accessed: 29 June 2018.
Gao, G. and M. Wüthrich. 2017. Feature Extraction from Telematics Car Driving Heatmaps. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3070069. Accessed: June 29 2018.
Gao, G. and M.V. Wüthrich. 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data", Risks 7(1):6.
Gao, G., M.V. Wuthrich and H. Yang. 2018. "Driving Risk Evaluation Based on Telematics Data", Available at SSRN 3288347
Gisler, A. and M. Wüthrich. 2008. "Credibility for the chain ladder reserving method", ASTIN Bulletin: The Journal of the IAA 38(2):565-600.
Golden, L., P. Brockett, J. Ai and B. Kellison. 2016. "Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations", North American Actuarial Journal 20(3):233-251.
Goodfellow, I., Y. Bengio and A. Courville. 2016. Deep Learning. MIT Press.
Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu et al. 2014. "Generative adversarial nets," Paper presented at Advances in Neural Information Processing Systems. 2672-2680.
Greff, K., R. Srivastava and J. Schmidhuber. 2016. "Highway and residual networks learn unrolled iterative estimation", arXiv preprint arXiv:1612.07771
Guo, C. and F. Berkhahn. 2016. "Entity embeddings of categorical variables", arXiv arXiv:1604.06737
Hainaut, D. 2018. "A neural-network analyzer for mortality forecast", Astin Bulletin 48(2):481-508.
Hannun, A., C. Case, J. Casper, B. Catanzaro et al. 2014. "Deep speech: Scaling up end-to-end speech recognition", arXiv arXiv:1412.5567
Hejazi, S. and K. Jackson. 2016. "A neural network approach to efficient valuation of large portfolios of variable annuities", Insurance: Mathematics and Economics 70:169-181.
Hejazi, S. and K. Jackson. 2017. "Efficient valuation of SCR via a neural network approach", Journal of Computational and Applied Mathematics 313:427-439.
Hinton, G. and R. Salakhutdinov. 2006. "Reducing the dimensionality of data with neural networks", Science 313(5786):504-507.
Ioffe, S. and C. Szegedy. 2015. "Batch normalization: Accelerating deep network training by reducing internal covariate shift", arXiv preprint arXiv:1502.03167
Kendall, A. and Y. Gal. 2017. "What uncertainties do we need in bayesian deep learning for computer vision?," Paper presented at Advances in neural information processing systems. 5574-5584.
Kingma, D. and M. Welling. 2013. "Auto-encoding variational bayes", arXiv preprint arXiv:1312.6114
Kleinberg, J., J. Ludwig, S. Mullainathan and Z. Obermeyer. 2015. "Prediction policy problems", American Economic Review 105(5):491-495.
Kleinow, T. 2015. "A common age effect model for the mortality of multiple populations", Insurance: Mathematics and Economics 63:147-152.
Krizhevsky, A., I. Sutskever and G. Hinton. 2012. "Imagenet classification with deep convolutional neural networks," Paper presented at Advances in Neural Information Processing Systems. 1097-1105.
Kuo, K. 2018. "DeepTriangle: A Deep Learning Approach to Loss Reserving", arXiv arXiv:1804.09253
LeCun, Y., Y. Bengio and G. Hinton. 2015. "Deep Learning", Nature 521(7553):436.
Li, N. and R. Lee. 2005. "Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method", Demography 42(3):575-594.
Makridakis, S., E. Spiliotis and V. Assimakopoulos. 2018. "The M4 Competition: Results, findings, conclusion and way forward", International Journal of Forecasting
Meyers, G. 2015. Stochastic loss reserving using Bayesian MCMC models. Casualty Actuarial Society New York.
Miranda, M., J. Nielsen and R. Verrall. 2013. "Double Chain Ladder", Astin Bulletin 42(1):59-76.
Mitchell, T. 1997. Machine learning. McGraw-Hill Boston, MA.
Nigri, A., S. Levantesi, M. Marino, S. Scognamiglio et al. 2019. "A Deep Learning Integrated Lee–Carter Model", Risks 7(1):33.
Noll, A., R. Salzmann and M. Wüthrich. 2018. Case Study: French Motor Third-Party Liability Claims. SSRN. https://ssrn.com/abstract=3164764 Accessed: 17 June 2018.
Pan, S. and Q. Yang. 2009. "A survey on transfer learning", IEEE Transactions on knowledge and data engineering 22(10):1345-1359.
Quarg, G. and T. Mack. 2004. "Munich chain ladder", Blätter der DGVFM 26(4):597-630.
Ribeiro, M.T., S. Singh and C. Guestrin. 2016. "Why should I trust you?: Explaining the predictions of any classifier," Paper presented at Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM. 1135-1144.
Ribeiro, M.T., S. Singh and C. Guestrin. 2018. "Anchors: High-precision model-agnostic explanations," Paper presented at Thirty-Second AAAI Conference on Artificial Intelligence.
Richman, R. and M. Wüthrich. 2018. "A Neural Network Extension of the Lee-Carter Model to Multiple Populations", Available at SSRN: https://ssrn.com/abstract=3270877
Schelldorfer, J. and M.V. Wüthrich. 2019. "Nesting Classical Actuarial Models into Neural Networks", Available at SSRN: https://ssrn.com/abstract=3320525
Shmueli, G. 2010. "To explain or to predict?", Statistical Science:289-310.
Smith, M., F. Beyers and J. De Villiers. 2016. "A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets", South African Actuarial Journal 16(1):35-67.
Smyl, S. 2018. "Exponential Smoothing - Recurrent Neural Network (ES-RNN) Hybrid Models", Github
Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever et al. 2014. "Dropout: a simple way to prevent neural networks from overfitting", The Journal of Machine Learning Research 15(1):1929-1958.
Stone, M. and R.J. Brooks. 1990. "Continuum Regression: Cross-Validated Sequentially Constructed Prediction Embracing Ordinary Least Squares, Partial Least Squares and Principal Components Regression", Journal of the Royal Statistical Society: Series B (Methodological) 52(2):237-258.
Veit, A., M. Wilber and S. Belongie. 2016. "Residual networks behave like ensembles of relatively shallow networks," Paper presented at Advances in neural information processing systems. 550-558.
Villegas, A.M., S. Haberman, V.K. Kaishev and P. Millossovich. 2017. "A comparative study of two-population models for the assessment of basis risk in longevity hedges", ASTIN Bulletin: The Journal of the IAA 47(3):631-679.
Wijnands, J., J. Thompson, G. Aschwanden and M. Stevenson. 2018. "Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks", Transportation Research Part F: Traffic Psychology and Behaviour 53:34-49.
Wu, Y., M. Schuster, Z. Chen, Q. Le et al. 2016. "Google's neural machine translation system: Bridging the gap between human and machine translation", arXiv arXiv:1609.08144
Wüthrich, M. 2018. "Neural networks applied to chain–ladder reserving", European Actuarial Journal 8(2):407-436.
Wüthrich, M. 2019. "Bias Regularization in Neural Network Models for General Insurance Pricing", Available at SSRN 3347177
Wüthrich, M. and C. Buser. 2018. Data analytics for non-life insurance pricing. Swiss Finance Institute Research Paper. https://ssrn.com/abstract=2870308. Accessed: 17 June 2018.
Wüthrich, M. and M. Merz. 2019. "Yes, we CANN!", ASTIN Bulletin: The Journal of the IAA 49(1):1-3.
Wüthrich, M.V. 2017. "Covariate selection from telematics car driving data", European Actuarial Journal 7(1):89-108.
Wüthrich, M.V. and M. Merz. 2018. "Editorial: Yes, we CANN!", Astin Bulletin:1-3.
Zarkadoulas, A. 2017. "Neural network algorithms for the development of individual losses." Unpublished thesis, Lausanne: University of Lausanne.
	Bahdanau, D., K. Cho and Y. Bengio. 2014. "Neural machine translation by jointly learning to align and translate", arXiv preprint arXiv:1409.0473
	Bengio, Y. 2009. "Learning deep architectures for AI", Foundations and trends® in Machine Learning 2(1):1-127.
	Bengio, Y. 2012. "Deep learning of representations for unsupervised and transfer learning," Paper presented at Proceedings of ICML Workshop on Unsupervised and Transfer Learning. 17-36.
	Bengio, Y., A. Courville and P. Vincent. 2013. "Representation learning: A review and new perspectives", IEEE transactions on pattern analysis and machine intelligence 35(8):1798-1828.
	Brass, W. 1964. "Uses of census or survey data for the estimation of vital rates", Paper presented at African Seminar on Vital Statistics. Addis Ababa, 14-19 December 1964. United Nations.
	Breeden, J.L. and E. Leonova. 2019. "When Big Data Isn’t Enough: Solving the long-range forecasting problem in supervised learning," Paper presented at 2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques (SMONT 2019). Atlantis Press.
	Breiman, L. 2001. "Statistical modeling: The two cultures (with comments and a rejoinder by the author)", Statistical Science 16(3):199-231.
	Castellani, G., U. Fiore, Z. Marino, L. Passalacqua et al. 2018. "An investigation of Machine Learning Approaches in the Solvency II Valuation Framework", Available at SSRN 3303296
	Chung, J., C. Gulcehre, K. Cho and Y. Bengio. 2015. "Gated feedback recurrent neural networks," Paper presented at International Conference on Machine Learning. 2067-2075.
	De Brébisson, A., É. Simon, A. Auvolat, P. Vincent et al. 2015. "Artificial neural networks applied to taxi destination prediction", arXiv arXiv:1508.00021
	Donahue, J., P. Krähenbühl and T. Darrell. 2016. "Adversarial feature learning", arXiv preprint arXiv:1605.09782
	Dong, W., J. Li, R. Yao, C. Li et al. 2016. "Characterizing driving styles with deep learning", arXiv arXiv:1607.03611
	Dong, W., T. Yuan, K. Yang, C. Li et al. 2017. "Autoencoder regularized network for driving style representation learning", arXiv arXiv:1701.01272
	Doyle, D. and C. Groendyke. 2018. "Using Neural Networks to Price and Hedge Variable Annuity Guarantees", Risks 7(1):1.
	Ezzini, S., I. Berrada and M. Ghogho. 2018. "Who is behind the wheel? Driver identification and fingerprinting", Journal of Big Data 5(1):9.
	Ferrario, A., A. Noll and M.V. Wuthrich. 2018. "Insights from Inside Neural Networks",
	Friedman, J., T. Hastie and R. Tibshirani. 2009. The Elements of Statistical Learning : Data Mining, Inference, and Prediction. New York: Springer-Verlag. http://dx.doi.org/10.1007/978-0-387-84858-7.
	Gabrielli, A. 2019. "A Neural Network Boosted Double Over-Dispersed Poisson Claims Reserving Model", Available at SSRN: https://ssrn.com/abstract=3365517
	Gabrielli, A., R. Richman and M.V. Wuthrich. 2018. "Neural network embedding of the over-dispersed Poisson reserving model", Available at SSRN: https://ssrn.com/abstract=3288454
	Gabrielli, A. and M. Wüthrich. 2018. "An Individual Claims History Simulation Machine", Risks 6(2):29.
	Gal, Y. 2016. "Uncertainty in deep learning." Unpublished thesis, PhD thesis, University of Cambridge.
	Gan, G. and X. Lin. 2015. "Valuation of large variable annuity portfolios under nested simulation: A functional data approach", Insurance: Mathematics and Economics 62:138-150.
	Gao, G., S. Meng and M. Wüthrich. 2018. Claims Frequency Modeling Using Telematics Car Driving Data. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3102371. Accessed: 29 June 2018.
	Gao, G. and M. Wüthrich. 2017. Feature Extraction from Telematics Car Driving Heatmaps. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3070069. Accessed: June 29 2018.
	Gao, G. and M.V. Wüthrich. 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data", Risks 7(1):6.
	Gao, G., M.V. Wuthrich and H. Yang. 2018. "Driving Risk Evaluation Based on Telematics Data", Available at SSRN 3288347
	Gisler, A. and M. Wüthrich. 2008. "Credibility for the chain ladder reserving method", ASTIN Bulletin: The Journal of the IAA 38(2):565-600.
	Golden, L., P. Brockett, J. Ai and B. Kellison. 2016. "Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations", North American Actuarial Journal 20(3):233-251.
	Goodfellow, I., Y. Bengio and A. Courville. 2016. Deep Learning. MIT Press.
	Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu et al. 2014. "Generative adversarial nets," Paper presented at Advances in Neural Information Processing Systems. 2672-2680.
	Greff, K., R. Srivastava and J. Schmidhuber. 2016. "Highway and residual networks learn unrolled iterative estimation", arXiv preprint arXiv:1612.07771
	Guo, C. and F. Berkhahn. 2016. "Entity embeddings of categorical variables", arXiv arXiv:1604.06737
	Hainaut, D. 2018. "A neural-network analyzer for mortality forecast", Astin Bulletin 48(2):481-508.
	Hannun, A., C. Case, J. Casper, B. Catanzaro et al. 2014. "Deep speech: Scaling up end-to-end speech recognition", arXiv arXiv:1412.5567
	Hejazi, S. and K. Jackson. 2016. "A neural network approach to efficient valuation of large portfolios of variable annuities", Insurance: Mathematics and Economics 70:169-181.
	Hejazi, S. and K. Jackson. 2017. "Efficient valuation of SCR via a neural network approach", Journal of Computational and Applied Mathematics 313:427-439.
	Hinton, G. and R. Salakhutdinov. 2006. "Reducing the dimensionality of data with neural networks", Science 313(5786):504-507.
	Ioffe, S. and C. Szegedy. 2015. "Batch normalization: Accelerating deep network training by reducing internal covariate shift", arXiv preprint arXiv:1502.03167
	Kendall, A. and Y. Gal. 2017. "What uncertainties do we need in bayesian deep learning for computer vision?," Paper presented at Advances in neural information processing systems. 5574-5584.
	Kingma, D. and M. Welling. 2013. "Auto-encoding variational bayes", arXiv preprint arXiv:1312.6114
	Kleinberg, J., J. Ludwig, S. Mullainathan and Z. Obermeyer. 2015. "Prediction policy problems", American Economic Review 105(5):491-495.
	Kleinow, T. 2015. "A common age effect model for the mortality of multiple populations", Insurance: Mathematics and Economics 63:147-152.
	Krizhevsky, A., I. Sutskever and G. Hinton. 2012. "Imagenet classification with deep convolutional neural networks," Paper presented at Advances in Neural Information Processing Systems. 1097-1105.
	Kuo, K. 2018. "DeepTriangle: A Deep Learning Approach to Loss Reserving", arXiv arXiv:1804.09253
	LeCun, Y., Y. Bengio and G. Hinton. 2015. "Deep Learning", Nature 521(7553):436.
	Li, N. and R. Lee. 2005. "Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method", Demography 42(3):575-594.
	Makridakis, S., E. Spiliotis and V. Assimakopoulos. 2018. "The M4 Competition: Results, findings, conclusion and way forward", International Journal of Forecasting
	Meyers, G. 2015. Stochastic loss reserving using Bayesian MCMC models. Casualty Actuarial Society New York.
	Miranda, M., J. Nielsen and R. Verrall. 2013. "Double Chain Ladder", Astin Bulletin 42(1):59-76.
	Mitchell, T. 1997. Machine learning. McGraw-Hill Boston, MA.
	Nigri, A., S. Levantesi, M. Marino, S. Scognamiglio et al. 2019. "A Deep Learning Integrated Lee–Carter Model", Risks 7(1):33.
	Noll, A., R. Salzmann and M. Wüthrich. 2018. Case Study: French Motor Third-Party Liability Claims. SSRN. https://ssrn.com/abstract=3164764 Accessed: 17 June 2018.
	Pan, S. and Q. Yang. 2009. "A survey on transfer learning", IEEE Transactions on knowledge and data engineering 22(10):1345-1359.
	Quarg, G. and T. Mack. 2004. "Munich chain ladder", Blätter der DGVFM 26(4):597-630.
	Ribeiro, M.T., S. Singh and C. Guestrin. 2016. "Why should I trust you?: Explaining the predictions of any classifier," Paper presented at Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM. 1135-1144.
	Ribeiro, M.T., S. Singh and C. Guestrin. 2018. "Anchors: High-precision model-agnostic explanations," Paper presented at Thirty-Second AAAI Conference on Artificial Intelligence.
	Richman, R. and M. Wüthrich. 2018. "A Neural Network Extension of the Lee-Carter Model to Multiple Populations", Available at SSRN: https://ssrn.com/abstract=3270877
	Schelldorfer, J. and M.V. Wüthrich. 2019. "Nesting Classical Actuarial Models into Neural Networks", Available at SSRN: https://ssrn.com/abstract=3320525
	Shmueli, G. 2010. "To explain or to predict?", Statistical Science:289-310.
	Smith, M., F. Beyers and J. De Villiers. 2016. "A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets", South African Actuarial Journal 16(1):35-67.
	Smyl, S. 2018. "Exponential Smoothing - Recurrent Neural Network (ES-RNN) Hybrid Models", Github
	Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever et al. 2014. "Dropout: a simple way to prevent neural networks from overfitting", The Journal of Machine Learning Research 15(1):1929-1958.
	Stone, M. and R.J. Brooks. 1990. "Continuum Regression: Cross-Validated Sequentially Constructed Prediction Embracing Ordinary Least Squares, Partial Least Squares and Principal Components Regression", Journal of the Royal Statistical Society: Series B (Methodological) 52(2):237-258.
	Veit, A., M. Wilber and S. Belongie. 2016. "Residual networks behave like ensembles of relatively shallow networks," Paper presented at Advances in neural information processing systems. 550-558.
	Villegas, A.M., S. Haberman, V.K. Kaishev and P. Millossovich. 2017. "A comparative study of two-population models for the assessment of basis risk in longevity hedges", ASTIN Bulletin: The Journal of the IAA 47(3):631-679.
	Wijnands, J., J. Thompson, G. Aschwanden and M. Stevenson. 2018. "Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks", Transportation Research Part F: Traffic Psychology and Behaviour 53:34-49.
	Wu, Y., M. Schuster, Z. Chen, Q. Le et al. 2016. "Google's neural machine translation system: Bridging the gap between human and machine translation", arXiv arXiv:1609.08144
	Wüthrich, M. 2018. "Neural networks applied to chain–ladder reserving", European Actuarial Journal 8(2):407-436.
	Wüthrich, M. 2019. "Bias Regularization in Neural Network Models for General Insurance Pricing", Available at SSRN 3347177
	Wüthrich, M. and C. Buser. 2018. Data analytics for non-life insurance pricing. Swiss Finance Institute Research Paper. https://ssrn.com/abstract=2870308. Accessed: 17 June 2018.
	Wüthrich, M. and M. Merz. 2019. "Yes, we CANN!", ASTIN Bulletin: The Journal of the IAA 49(1):1-3.
	Wüthrich, M.V. 2017. "Covariate selection from telematics car driving data", European Actuarial Journal 7(1):89-108.
	Wüthrich, M.V. and M. Merz. 2018. "Editorial: Yes, we CANN!", Astin Bulletin:1-3.
	Zarkadoulas, A. 2017. "Neural network algorithms for the development of individual losses." Unpublished thesis, Lausanne: University of Lausanne.