{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stochastic-variational-deep-kernel-learning","title":"Stochastic Variational Deep Kernel Learning","arxiv_id":"1611.00336","date":"2016-11-01","proceeding":"NeurIPS 2016 12","authors":["Andrew Gordon Wilson","Zhiting Hu","Ruslan Salakhutdinov","Eric P. Xing"],"abstract":"Deep kernel learning combines the non-parametric flexibility of kernel\nmethods with the inductive biases of deep learning architectures. We propose a\nnovel deep kernel learning model and stochastic variational inference procedure\nwhich generalizes deep kernel learning approaches to enable classification,\nmulti-task learning, additive covariance structures, and stochastic gradient\ntraining. Specifically, we apply additive base kernels to subsets of output\nfeatures from deep neural architectures, and jointly learn the parameters of\nthe base kernels and deep network through a Gaussian process marginal\nlikelihood objective. Within this framework, we derive an efficient form of\nstochastic variational inference which leverages local kernel interpolation,\ninducing points, and structure exploiting algebra. We show improved performance\nover stand alone deep networks, SVMs, and state of the art scalable Gaussian\nprocesses on several classification benchmarks, including an airline delay\ndataset containing 6 million training points, CIFAR, and ImageNet.","url_abs":"http://arxiv.org/abs/1611.00336v2","url_pdf":"http://arxiv.org/pdf/1611.00336v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"dkl","method_name":"DKL"},{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[{"slug":"dkl","name":"DKL","full_name":"Deep Kernel Learning"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.00336","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}