{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T14:31:46Z","timestamp":1725805906439},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319118116"},{"type":"electronic","value":"9783319118123"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-11812-3_30","type":"book-chapter","created":{"date-parts":[[2014,9,27]],"date-time":"2014-09-27T03:03:16Z","timestamp":1411786996000},"page":"350-361","source":"Crossref","is-referenced-by-count":5,"title":["Variational Dependent Multi-output Gaussian Process Dynamical Systems"],"prefix":"10.1007","author":[{"given":"Jing","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiliang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"30_CR1","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1109\/TPAMI.2013.86","volume":"35","author":"M.A. \u00c1lvarez","year":"2013","unstructured":"\u00c1lvarez, M.A., Luengo, D., Lawrence, N.D.: Linear latent force models using Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence\u00a035, 2693\u20132705 (2013)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"30_CR2","first-page":"1459","volume":"12","author":"M.A. \u00c1lvarez","year":"2011","unstructured":"\u00c1lvarez, M.A., Lawrence, N.D.: Computationally efficient convolved multiple output Gaussian processes. Journal of Machine Learning Research\u00a012, 1459\u20131500 (2011)","journal-title":"Journal of Machine Learning Research"},{"key":"30_CR3","unstructured":"\u00c1lvarez, M.A., Luengo, D., Lawrence, N.D.: Latent force models. In: Proceedings of the 12th International Conference on Articicial Intelligence and Statistics, pp. 9\u201316 (2009)"},{"key":"30_CR4","unstructured":"Bonilla, E.V., Chai, K.M., Williams, C.K.I.: Multi-task Gaussian process prediction. In: Advances in Neural Information Processing Systems, vol.\u00a018, pp. 153\u2013160 (2008)"},{"key":"30_CR5","unstructured":"Damianou, A.C., Ek, C.H., Titsias, M.K., Lawrence, N.D.: Manifold relevance determination. In: Proceedings of the 29th International Conference on Machine Learning, pp. 145\u2013152 (2012)"},{"key":"30_CR6","unstructured":"Damianou, A.C., Titsias, M.K., Lawrence, N.D.: Variational Gaussian process dynamical systems. In: Advances in Neural Information Processing Systems, vol.\u00a024, pp. 2510\u20132518 (2011)"},{"key":"30_CR7","unstructured":"Deisenroth, M.P., Mohamed, S.: Expectation propagation in Gaussian process dynamical systems. In: Advances in Neural Information Processing Systems, vol.\u00a025, pp. 2618\u20132626 (2012)"},{"key":"30_CR8","unstructured":"Hartikainen, J., S\u00e4rkk\u00e4, S.: Sequential inference for latent force models (2012), http:\/\/arxiv.org\/abs\/1202.3730"},{"key":"30_CR9","unstructured":"Lawrence, N.D.: Gaussian process latent variable models for visualisation of high dimensional data. In: Advances in Neural Information Processing Systems, vol.\u00a017, pp. 329\u2013336 (2004)"},{"key":"30_CR10","first-page":"1783","volume":"6","author":"N.D. Lawrence","year":"2005","unstructured":"Lawrence, N.D.: Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research\u00a06, 1783\u20131816 (2005)","journal-title":"Journal of Machine Learning Research"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Lawrence, N.D.: Learning for larger dataset with the Gaussian process latent variable model. In: Proceedings of the 11th International Workshop on Artificial Intelligence and Statistics, pp. 243\u2013250 (2007)","DOI":"10.1145\/1273496.1273557"},{"key":"30_CR12","unstructured":"Luttinen, J., Ilin, A.: Efficient Gaussian process inference for short-scale spatio-temporal modeling. In: Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, pp. 741\u2013750 (2012)"},{"key":"30_CR13","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1162\/neco.2008.08-07-592","volume":"21","author":"M. Opper","year":"2009","unstructured":"Opper, M., Archambeau, A.: The variational Gaussian approximation revisited. Neural Computation\u00a021, 786\u2013792 (2009)","journal-title":"Neural Computation"},{"key":"30_CR14","unstructured":"Park, H., Yun, S., Park, S., Kim, J., Yoo, C.D.: Phoneme classification using constrained variational Gaussian process dynamical system. In: Advances in Neural Information Processing Systems, vol.\u00a022, pp. 2015\u20132023 (2012)"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Process for Machine Learning. MIT Press (2006)","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"30_CR16","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1007\/s00521-013-1445-4","volume":"23","author":"S. Sun","year":"2013","unstructured":"Sun, S.: A review of deterministic approximate inference techniques for Bayesian machine learning. Neural Computing and Applications\u00a023, 2039\u20132050 (2013)","journal-title":"Neural Computing and Applications"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Taylor, G.W., Hinton, G.E., Roweis, S.: Modeling human motion using binary latent variables. In: Advances in Neural Information Processing Systems, vol.\u00a017, pp. 1345\u20131352 (2007)","DOI":"10.7551\/mitpress\/7503.003.0173"},{"key":"30_CR18","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1111\/1467-9868.00196","volume":"61","author":"M.E. Tipping","year":"1999","unstructured":"Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society\u00a061, 611\u2013622 (1999)","journal-title":"Journal of the Royal Statistical Society"},{"key":"30_CR19","unstructured":"Titsias, M.K.: Variational learning of inducing variables in sparse Gaussian processes. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, pp. 567\u2013574 (2009)"},{"key":"30_CR20","unstructured":"Titsias, M.K., Lawrence, N.D.: Bayesian Gaussian process latent variable model. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, pp. 844\u2013851 (2010)"},{"key":"30_CR21","unstructured":"Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian process dynamical models. In: Advances in Neural Information Processing Systems, vol.\u00a019, pp. 1441\u20131448 (2006)"},{"key":"30_CR22","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1109\/TPAMI.2007.1167","volume":"30","author":"J.M. Wang","year":"2008","unstructured":"Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian process dynamical models for human motion. IEEE Transactions on Pattern Analysis and Machine Intelligence\u00a030, 283\u2013398 (2008)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"30_CR23","unstructured":"Wilson, A.G., Knowles, D.A., Ghahramani, Z.: Gaussian process regression networks. In: Proceedings of the 29th International Conference on Machine Learning, pp. 599\u2013606 (2012)"}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-11812-3_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,16]],"date-time":"2023-07-16T21:17:23Z","timestamp":1689542243000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-11812-3_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319118116","9783319118123"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-11812-3_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2014]]}}}