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.topic_pill.topic_pill a.topic_pill:hover a.action_button.action_button:active.action_button:hover.action_button:focus.action_button:hover.action_button:focus .count.action_button:hover .count.action_button:focus .count:before.action_button:hover .count:before.submit_button.submit_button:active.submit_button:hover.submit_button:not(.fake_disabled):hover.submit_button:not(.fake_disabled):focus._type_serif_title_large.js-wf-loaded ._type_serif_title_large.amp-page [email protected] only screen and (min-device-width:320px) and (max-device-width:360px).u-margin-top--lg.u-margin-left--sm.u-flex.u-flex-auto.u-flex-none.bullet. Content Wrapper:after.hidden.normal.grid_page.grid_page:before,.grid_page:after.grid_page:after.grid_page h3.grid_page h3 a.grid_page h3 a:hover.grid_page h3 a.action_button.grid_page h3 a.action_button:active.grid_page h3 a.action_button:hover.grid_page h3 a.action_button:not(.fake_disabled):hover.grid_page h3 a.action_button:not(.fake_disabled):focus.grid_pagediv. The proposed framework is experimentally validated using synthetic and real-world datasets.Comment: The first two authors contributed equally.In this way all of the approximation is performed at `inference time' rather than at `modelling time', resolving awkward philosophical and empirical questions that trouble previous approaches. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information.

The unreasonable effectiveness of structured random orthogonal embeddings. Abstract: We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation.

The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline.

The result clearly shows that the IMEKF and the NLS-based method are superior to q-IEKF and all three outperform the non-iterative methods.

[ GPs | Clustering | Graphical Models | MCMC | Semi-Supervised | Non-Parametric | Approximations | Bioinformatics | Information Retreival | RL and Control | Time Series | Network Modelling | Active Learning | Neuroscience | Signal Processing | Machine Vision | Machine Hearing | NLP | Deep Learning | Review ] [ Balog | Bauer | Bui | Dziugaite | Ge | Ghahramani | Gu | Hernández-Lobato | Kilbertus | Kok | Li | Lomeli | Matthews | Navarro | Peharz | Rasmussen | Rojas-Carulla | Rowland | Ścibior | Shah | Steinrücken | Rich Turner | Weller ] [ Borgwardt | Bratières | Calliess | Chen | Cunningham | Davies | Deisenroth | Duvenaud | Eaton | Frellsen | Frigola | Van Gael | Gal | Heaukulani | Heller | Hoffman | Houlsby | Huszár | Knowles | Lacoste-Julien | Lloyd | Lopez-Paz | Mc Allister | Mc Hutchon | Mohamed | Orbanz | Ortega | Palla | Quadrianto | Roy | Saatçi | Tobar | Ryan Turner | Snelson | van der Wilk | Williamson | Wilson ] Gaussian processes are non-parametric distributions useful for doing Bayesian inference and learning on unknown functions.

They can be used for non-linear regression, time-series modelling, classification, and many other problems., volume 31, Long Beach, California, USA, December 2017. Error Banner.fade_out.modal_overlay.modal_overlay .modal_wrapper.modal_overlay [email protected](max-width:630px)@media(max-width:630px).modal_overlay .modal_fixed_close.modal_overlay .modal_fixed_close:before.modal_overlay .modal_fixed_close:before.modal_overlay .modal_fixed_close:before.modal_overlay .modal_fixed_close:hover:before.



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