Student-t VAEによるロバスト確率密度推定
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| Published in | 人工知能学会論文誌 Vol. 36; no. 3; pp. A-KA4_1 - 9 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Japanese |
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一般社団法人 人工知能学会
01.05.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1346-0714 1346-8030 |
| DOI | 10.1527/tjsai.36-3_A-KA4 |
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| Author | 岩田, 具治 高橋, 大志 山田, 真徳 鹿島, 久嗣 山中, 友貴 八木, 哲志 |
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| References | [Martinez-Cantin 17] Martinez-Cantin, R., McCourt, M., and Tee, K.: Robust Bayesian optimization with Student-t likelihood, arXiv preprint arXiv:1707.05729 (2017) [Johnson 13] Johnson, R. and Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction, in Advances in Neural Information Processing Systems, pp. 315–323 (2013) [Lichman13] Lichman, M.: UCI Machine Learning Repository (2013) [Duchi 11] Duchi, J., Hazan, E., and Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization, Journal of Machine Learning Research, Vol. 12, No. Jul, pp. 2121–2159 (2011) [Oord16] Oord, van den A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with PixelCNN decoders, in Advances in Neural Information Processing Systems, pp. 4790–4798 (2016) [Kim 12] Kim, J. and Scott, C. D.: Robust kernel density estimation, Journal of Machine Learning Research, Vol. 13, No. 1, pp. 2529– 2565 (2012) [Reynolds 00] Reynolds, D. A., Quatieri, T. F., and Dunn, R. B.: Speaker verification using adapted Gaussian mixture models, Digital Signal Processing, Vol. 10, No. 1-3, pp. 19–41 (2000) [Burda 15] Burda, Y., Grosse, R., and Salakhutdinov, R.: Importance weighted autoencoders, arXiv preprint arXiv:1509.00519 (2015) [Zeiler 12] Zeiler, M. D.: ADADELTA: an adaptive learning rate method, arXiv preprint arXiv:1212.5701 (2012) [Goldstein 16] Goldstein, M. and Uchida, S.: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data, PloS One, Vol. 11, No. 4, p. e0152173 (2016) [Yin 14] Yin, J. and Wang, J.: A dirichlet multinomial mixture model-based approach for short text clustering, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 233–242 (2014) [Goodfellow 16] Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press (2016), http://www.deeplearningbook.org [Rezende14] Rezende, D. J., Mohamed, S., and Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models, in Proceedings of the 31st International Conference on Machine Learning, pp. 1278–1286 (2014) [Lange 89] Lange, K. L., Little, R. J., and Taylor, J. M.: Robust statistical modeling using the t distribution, Journal of the American Statistical Association, Vol. 84, No. 408, pp. 881–896 (1989) [Zivkovic 04] Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction, in Proceedings of the 17th International Conference on Pattern Recognition 2004 (ICPR 2004), Vol. 2, pp. 28–31 (2004) [Roeder 17] Roeder, G., Wu, Y., and Duvenaud, D. K.: Sticking the landing: Simple, lower-variance gradient estimators for variational inference, in Advances in Neural Information Processing Systems, pp. 6928–6937 (2017) [Suh 16] Suh, S., Chae, D. H., Kang, H.-G., and Choi, S.: Echo-state conditional variational autoencoder for anomaly detection, in 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1015–1022 (2016) [Miller 17] Miller, A., Foti, N., D’Amour, A., and Adams, R. P.: Reducing reparameterization gradient variance, in Advances in Neural Information Processing Systems 30, pp. 3711–3721 (2017) [Scott 15] Scott, D. W.: Multivariate Density Estimation: Theory, Practice, and Visualization, John Wiley & Sons (2015) [Silverman 86] Silverman, B. W.: Density Estimation for Statistics and Data Analysis, Vol. 26, CRC Press (1986) [Wang 13] Wang, C., Chen, X., Smola, A. J., and Xing, E. P.: Variance reduction for stochastic gradient optimization, in Advances in Neural Information Processing Systems, pp. 181–189 (2013) [Kingma 14] Kingma, D. and Ba, J.: Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014) [Geusebroek 05] Geusebroek, J.-M., Burghouts, G. J., and Smeulders, A. W.: The Amsterdam library of object images, International Journal of Computer Vision, Vol. 61, No. 1, pp. 103–112 (2005) [Maaten 08] Maaten, L. v. d. and Hinton, G.: Visualizing data using t-SNE, Journal of Machine Learning Research, Vol. 9, No. Nov, pp.2579–2605 (2008) [Jylänki 11] Jylänki, P., Vanhatalo, J., and Vehtari, A.: Robust Gaussian process regression with a Student-t likelihood, Journal of Machine Learning Research, Vol. 12, No. Nov, pp. 3227–3257 (2011) [Salimans17] Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P.: PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications, arXiv preprint arXiv:1701.05517 (2017) [Kingma 13] Kingma, D. P. and Welling, M.: Auto-encoding variational Bayes, arXiv preprint arXiv:1312.6114 (2013) [Tieleman 12] Tieleman, T. and Hinton, G.: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural Networks for Machine Learning, Vol. 4, No. 2, pp. 26–31 (2012) [Pedregosa11] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, Vol. 12, No. Oct, pp. 2825–2830 (2011) [McLachlan 04] McLachlan, G. and Peel, D.: Finite Mixture Models, John Wiley & Sons (2004) [Kingma 15] Kingma, D. P., Salimans, T., and Welling, M.: Variational dropout and the local reparameterization trick, in Advances in Neural Information Processing Systems, pp. 2575–2583 (2015) |
| References_xml | – reference: [Burda 15] Burda, Y., Grosse, R., and Salakhutdinov, R.: Importance weighted autoencoders, arXiv preprint arXiv:1509.00519 (2015) – reference: [McLachlan 04] McLachlan, G. and Peel, D.: Finite Mixture Models, John Wiley & Sons (2004) – reference: [Reynolds 00] Reynolds, D. A., Quatieri, T. F., and Dunn, R. B.: Speaker verification using adapted Gaussian mixture models, Digital Signal Processing, Vol. 10, No. 1-3, pp. 19–41 (2000) – reference: [Tieleman 12] Tieleman, T. and Hinton, G.: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural Networks for Machine Learning, Vol. 4, No. 2, pp. 26–31 (2012) – reference: [Salimans17] Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P.: PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications, arXiv preprint arXiv:1701.05517 (2017) – reference: [Martinez-Cantin 17] Martinez-Cantin, R., McCourt, M., and Tee, K.: Robust Bayesian optimization with Student-t likelihood, arXiv preprint arXiv:1707.05729 (2017) – reference: [Oord16] Oord, van den A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with PixelCNN decoders, in Advances in Neural Information Processing Systems, pp. 4790–4798 (2016) – reference: [Maaten 08] Maaten, L. v. d. and Hinton, G.: Visualizing data using t-SNE, Journal of Machine Learning Research, Vol. 9, No. Nov, pp.2579–2605 (2008) – reference: [Geusebroek 05] Geusebroek, J.-M., Burghouts, G. J., and Smeulders, A. W.: The Amsterdam library of object images, International Journal of Computer Vision, Vol. 61, No. 1, pp. 103–112 (2005) – reference: [Silverman 86] Silverman, B. W.: Density Estimation for Statistics and Data Analysis, Vol. 26, CRC Press (1986) – reference: [Kingma 15] Kingma, D. P., Salimans, T., and Welling, M.: Variational dropout and the local reparameterization trick, in Advances in Neural Information Processing Systems, pp. 2575–2583 (2015) – reference: [Rezende14] Rezende, D. J., Mohamed, S., and Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models, in Proceedings of the 31st International Conference on Machine Learning, pp. 1278–1286 (2014) – reference: [Miller 17] Miller, A., Foti, N., D’Amour, A., and Adams, R. P.: Reducing reparameterization gradient variance, in Advances in Neural Information Processing Systems 30, pp. 3711–3721 (2017) – reference: [Roeder 17] Roeder, G., Wu, Y., and Duvenaud, D. K.: Sticking the landing: Simple, lower-variance gradient estimators for variational inference, in Advances in Neural Information Processing Systems, pp. 6928–6937 (2017) – reference: [Pedregosa11] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, Vol. 12, No. Oct, pp. 2825–2830 (2011) – reference: [Zeiler 12] Zeiler, M. D.: ADADELTA: an adaptive learning rate method, arXiv preprint arXiv:1212.5701 (2012) – reference: [Yin 14] Yin, J. and Wang, J.: A dirichlet multinomial mixture model-based approach for short text clustering, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 233–242 (2014) – reference: [Goldstein 16] Goldstein, M. and Uchida, S.: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data, PloS One, Vol. 11, No. 4, p. e0152173 (2016) – reference: [Johnson 13] Johnson, R. and Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction, in Advances in Neural Information Processing Systems, pp. 315–323 (2013) – reference: [Duchi 11] Duchi, J., Hazan, E., and Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization, Journal of Machine Learning Research, Vol. 12, No. Jul, pp. 2121–2159 (2011) – reference: [Jylänki 11] Jylänki, P., Vanhatalo, J., and Vehtari, A.: Robust Gaussian process regression with a Student-t likelihood, Journal of Machine Learning Research, Vol. 12, No. Nov, pp. 3227–3257 (2011) – reference: [Scott 15] Scott, D. W.: Multivariate Density Estimation: Theory, Practice, and Visualization, John Wiley & Sons (2015) – reference: [Kim 12] Kim, J. and Scott, C. D.: Robust kernel density estimation, Journal of Machine Learning Research, Vol. 13, No. 1, pp. 2529– 2565 (2012) – reference: [Kingma 14] Kingma, D. and Ba, J.: Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014) – reference: [Kingma 13] Kingma, D. P. and Welling, M.: Auto-encoding variational Bayes, arXiv preprint arXiv:1312.6114 (2013) – reference: [Lange 89] Lange, K. L., Little, R. J., and Taylor, J. M.: Robust statistical modeling using the t distribution, Journal of the American Statistical Association, Vol. 84, No. 408, pp. 881–896 (1989) – reference: [Goodfellow 16] Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press (2016), http://www.deeplearningbook.org – reference: [Wang 13] Wang, C., Chen, X., Smola, A. J., and Xing, E. P.: Variance reduction for stochastic gradient optimization, in Advances in Neural Information Processing Systems, pp. 181–189 (2013) – reference: [Suh 16] Suh, S., Chae, D. H., Kang, H.-G., and Choi, S.: Echo-state conditional variational autoencoder for anomaly detection, in 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1015–1022 (2016) – reference: [Lichman13] Lichman, M.: UCI Machine Learning Repository (2013) – reference: [Zivkovic 04] Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction, in Proceedings of the 17th International Conference on Pattern Recognition 2004 (ICPR 2004), Vol. 2, pp. 28–31 (2004) |
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| Title | Student-t VAEによるロバスト確率密度推定 |
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