{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T17:03:48Z","timestamp":1749575028562,"version":"3.37.3"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,1,8]],"date-time":"2019-01-08T00:00:00Z","timestamp":1546905600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,1,8]],"date-time":"2019-01-08T00:00:00Z","timestamp":1546905600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH115697"],"award-info":[{"award-number":["R01MH115697"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS1622490"],"award-info":[{"award-number":["DMS1622490"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Stat"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1007\/s00180-018-00861-z","type":"journal-article","created":{"date-parts":[[2019,1,9]],"date-time":"2019-01-09T04:14:02Z","timestamp":1547007242000},"page":"281-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Neural network gradient Hamiltonian Monte Carlo"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0747-3711","authenticated-orcid":false,"given":"Lingge","family":"Li","sequence":"first","affiliation":[]},{"given":"Andrew","family":"Holbrook","sequence":"additional","affiliation":[]},{"given":"Babak","family":"Shahbaba","sequence":"additional","affiliation":[]},{"given":"Pierre","family":"Baldi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,8]]},"reference":[{"key":"861_CR1","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.neunet.2016.07.006","volume":"83","author":"P Baldi","year":"2016","unstructured":"Baldi P, Sadowski P (2016) A theory of local learning, the learning channel, and the optimality of backpropagation. Neural Netw 83:51\u201374","journal-title":"Neural Netw"},{"unstructured":"Betancourt M (2015) The fundamental incompatibility of Hamiltonian Monte Carlo and data subsampling. arXiv preprint arXiv:1502.01510","key":"861_CR2"},{"unstructured":"Chen T, Fox E, Guestrin C (2014) Stochastic gradient Hamiltonian Monte Carlo. In: International conference on machine learning, pp 1683\u20131691","key":"861_CR3"},{"issue":"4","key":"861_CR4","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst (MCSS) 2(4):303\u2013314","journal-title":"Math Control Signals Syst (MCSS)"},{"unstructured":"Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of IEEE international joint conference on neural networks, IEEE, vol\u00a02, pp 985\u2013990","key":"861_CR5"},{"unstructured":"Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980","key":"861_CR6"},{"key":"861_CR7","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.jcp.2015.12.032","volume":"308","author":"S Lan","year":"2016","unstructured":"Lan S, Bui-Thanh T, Christie M, Girolami M (2016) Emulation of higher-order tensors in manifold monte carlo methods for bayesian inverse problems. J Comput Phys 308:81\u2013101","journal-title":"J Comput Phys"},{"key":"861_CR8","volume-title":"Simulating Hamiltonian dynamics","author":"B Leimkuhler","year":"2004","unstructured":"Leimkuhler B, Reich S (2004) Simulating Hamiltonian dynamics, vol 14. Cambridge University Press, Cambridge"},{"key":"861_CR9","volume-title":"Bayesian learning for neural networks","author":"RM Neal","year":"2012","unstructured":"Neal RM (2012) Bayesian learning for neural networks, vol 118. Springer, New York"},{"key":"861_CR10","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1201\/b10905-6","volume":"2","author":"RM Neal","year":"2011","unstructured":"Neal RM et al (2011) Mcmc using Hamiltonian dynamics. Handbook Markov Chain Monte Carlo 2:113\u2013162","journal-title":"Handbook Markov Chain Monte Carlo"},{"key":"861_CR11","first-page":"651","volume":"7","author":"CE Rasmussen","year":"2003","unstructured":"Rasmussen CE, Bernardo J, Bayarri M, Berger J, Dawid A, Heckerman D, Smith A, West M (2003) Gaussian processes to speed up hybrid monte carlo for expensive bayesian integrals. Bayesian Stat 7:651\u2013659","journal-title":"Bayesian Stat"},{"unstructured":"Welling M, Teh YW (2011) Bayesian learning via stochastic gradient langevin dynamics. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 681\u2013688","key":"861_CR12"},{"doi-asserted-by":"publisher","unstructured":"Zhang C, Shahbaba B, Zhao H (2017) Hamiltonian monte carlo acceleration using surrogate functions with random bases. Stat Comput 27:1473. https:\/\/doi.org\/10.1007\/s11222-016-9699-1","key":"861_CR13","DOI":"10.1007\/s11222-016-9699-1"}],"container-title":["Computational Statistics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00180-018-00861-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-018-00861-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-018-00861-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T22:08:54Z","timestamp":1720908534000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00180-018-00861-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,8]]},"references-count":13,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,3]]}},"alternative-id":["861"],"URL":"https:\/\/doi.org\/10.1007\/s00180-018-00861-z","relation":{},"ISSN":["0943-4062","1613-9658"],"issn-type":[{"type":"print","value":"0943-4062"},{"type":"electronic","value":"1613-9658"}],"subject":[],"published":{"date-parts":[[2019,1,8]]},"assertion":[{"value":"26 November 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}