{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:37:56Z","timestamp":1775871476790,"version":"3.50.1"},"reference-count":26,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7]]},"DOI":"10.1109\/ijcnn.2016.7727626","type":"proceedings-article","created":{"date-parts":[[2016,11,8]],"date-time":"2016-11-08T21:15:56Z","timestamp":1478639756000},"page":"3338-3345","source":"Crossref","is-referenced-by-count":107,"title":["Manifold Gaussian Processes for regression"],"prefix":"10.1109","author":[{"given":"Roberto","family":"Calandra","sequence":"first","affiliation":[]},{"given":"Jan","family":"Peters","sequence":"additional","affiliation":[]},{"given":"Carl Edward","family":"Rasmussen","sequence":"additional","affiliation":[]},{"given":"Marc Peter","family":"Deisenroth","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","author":"neal","year":"1995","journal-title":"Bayesian learning for neural networks"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/14786440109462720"},{"key":"ref12","article-title":"Gaussian Processes for Machine Learning","author":"rasmussen","year":"2006","journal-title":"MIT Press"},{"key":"ref13","author":"renjewski","year":"2012","journal-title":"An engineering contribution to human gait biomechanics"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"ref15","article-title":"Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes","author":"salakhutdinov","year":"2007","journal-title":"NIPS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00413"},{"key":"ref17","article-title":"Warped Gaussian Processes","author":"snelson","year":"2004","journal-title":"NIPS"},{"key":"ref18","article-title":"Variable Noise and Dimensionality Reduction for Sparse Gaussian Processes","author":"snelson","year":"2006","journal-title":"UAI"},{"key":"ref19","first-page":"2567","article-title":"Nonparametric Guidance of Autoencoder Representations using Label Information","volume":"13","author":"snoek","year":"2012","journal-title":"JMLR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(00)00026-5"},{"key":"ref3","article-title":"Approximate Infer-ence for Long-Term Forecasting with Periodic Gaussian Processes","author":"hajighassemi","year":"2014","journal-title":"AISTATS"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589116"},{"key":"ref5","first-page":"1783","article-title":"Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models","volume":"6","author":"lawrence","year":"2005","journal-title":"JMLR"},{"key":"ref8","article-title":"Deep Learning via Hessian-free Optimization","author":"martens","year":"2010","journal-title":"ICML"},{"key":"ref7","first-page":"133","article-title":"Introduction to Gaussian Processes","volume":"168","author":"mackay","year":"1998","journal-title":"Neural Networks and Machine Learning"},{"key":"ref2","article-title":"Structure Discovery in Nonparametric Regression through Compositional Kernel Search","author":"duvenaud","year":"2013","journal-title":"ICML"},{"key":"ref9","article-title":"Gaussian Process Training with Input Noise","author":"mchutchon","year":"2011","journal-title":"NIPS"},{"key":"ref1","article-title":"Deep Gaussian Processes","author":"damianou","year":"2013","journal-title":"AISTATS"},{"key":"ref20","article-title":"Input Warping for Bayesian Optimization of Non-stationary Functions","author":"snoek","year":"2014","journal-title":"ICML"},{"key":"ref22","article-title":"Bayesian Gaussian Process Latent Variable Model","author":"titsias","year":"2010","journal-title":"AISTATS"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A Global Geometric Framework for Nonlinear Dimensionality Re-duction","volume":"290","author":"tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref24","article-title":"Learning Deep Dynamical Models From Image Pixels","author":"wahlstr\u00f6m","year":"2015","journal-title":"SYSID"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"ref26","article-title":"Gaussian Process Kernels for Pattern Discovery and Extrapolation","author":"wilson","year":"2013","journal-title":"ICML"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1167"}],"event":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","location":"Vancouver, BC, Canada","start":{"date-parts":[[2016,7,24]]},"end":{"date-parts":[[2016,7,29]]}},"container-title":["2016 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7593175\/7726591\/07727626.pdf?arnumber=7727626","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,25]],"date-time":"2017-06-25T03:07:12Z","timestamp":1498360032000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7727626\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2016.7727626","relation":{},"subject":[],"published":{"date-parts":[[2016,7]]}}}