![]() Nguyen, C.V., Li, Y., Bui, T.D., Turner, R.E.: Variational continual learning. Maidstone, R., Hocking, T., Rigaill, G., Fearnhead, P.: On optimal multiple changepoint algorithms for large data. In: Advances in Neural Information Processing Systems, pp. Lopez-Paz, D.: et al.: Gradient episodic memory for continual learning. IEEE Transactions on Pattern Analysis and Machine Intelligence LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. LeCun, Y., Cortes, C.: MNIST handwritten digit database. Proceedings of the National Academy of Sciences p. ![]() et al.: Overcoming catastrophic forgetting in neural networks. Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A. (2014) Cite arxiv:1412.6980 comment: Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015 Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. Killick, R., Fearnhead, P., Eckley, I.: Optimal detection of changepoints with a linear computational cost. Kaplanis, C., Shanahan, M., Clopath, C.: Continual reinforcement learning with complex synapses. Jandhyala, V.K., Fotopoulos, S.B., Hawkins, D.M.: Detection and estimation of abrupt changes in the variability of a process. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society, pp. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Hawkins, D.M., Qiu, P., Kang, C.W.: The changepoint model for statistical process control. Goodfellow, I.J., Mirza, M., Xiao, D., Courville, A., Bengio, Y.: An empirical investigation of catastrophic forgetting in gradient-based neural networks. 16(2), 203–213 (2006)įearnhead, P., Liu, Z.: Online inference for multiple changepoint problems. arXiv preprint arXiv:1805.09733įearnhead, P.: Exact and efficient Bayesian inference for multiple changepoint problems. Biometrika 58(2), 341–348 (1971)įarquhar S, Gal Y (2018) Towards Robust Evaluations of Continual Learning. 613–622 (1973)ĭ’Agostino Ralph, B.: An omnibus test of normality for moderate and large size samples. empirical results for the distributions of b2 and b1. Wiley, Chichester (1997)ĭ’Agostino, R., Pearson, E.: Tests for departure from normality. Springer-Verlag, New York Inc, Secaucus, NJ, USA (2006)Ĭaron, F., Doucet, A., Gottardo, R.: On-line changepoint detection and parameter estimation with application to genomic data. 20(1), 260–279 (1992)īishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). 57(5), 1997–2043 (2002)īarry, D., Hartigan, J.A.: Product partition models for change point problems. 51(2), 339–367 (2017)īansal, R., Zhou, H.: Term structure of interest rates with regime shifts. CoRR arXiv:1903.08671 (2019)Īminikhanghahi, S., Cook, D.J.: A survey of methods for time series change point detection. (2018) CoRR arXiv:1812.03596Īljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. Stat 1050, 19 (2007)Īljundi, R., Kelchtermans, K., Tuytelaars, T.: Task-free continual learning. Adams, R.P., MacKay, D.J.: Bayesian online changepoint detection.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |