{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:21:39Z","timestamp":1759332099534,"version":"3.37.3"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["DMS-2015577"],"award-info":[{"award-number":["DMS-2015577"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency\u2019s Simplifying Complexity in Scientific Discovery","doi-asserted-by":"publisher","award":["N66001-15-C-4035"],"award-info":[{"award-number":["N66001-15-C-4035"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research Multidisciplinary University Initiative","doi-asserted-by":"publisher","award":["N00014-16-1-2007"],"award-info":[{"award-number":["N00014-16-1-2007"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"DARPA Army Research Office","doi-asserted-by":"publisher","award":["W911NF-16-1-0579"],"award-info":[{"award-number":["W911NF-16-1-0579"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"DARPA","doi-asserted-by":"publisher","award":["N66001-17-2-4029"],"award-info":[{"award-number":["N66001-17-2-4029"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Extreme Science and Engineering Discovery Environment","award":["CIS210052"],"award-info":[{"award-number":["CIS210052"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1109\/tnnls.2022.3168795","type":"journal-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T19:50:11Z","timestamp":1651780211000},"page":"10563-10577","source":"Crossref","is-referenced-by-count":7,"title":["Energy-Based Continuous Inverse Optimal Control"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2407-3266","authenticated-orcid":false,"given":"Yifei","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Statistics, University of California at Los Angeles, Los Angeles, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6818-7444","authenticated-orcid":false,"given":"Jianwen","family":"Xie","sequence":"additional","affiliation":[{"name":"Cognitive Computing Laboratory, Baidu Research, Bellevue, WA, USA"}]},{"given":"Tianyang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California at Los Angeles, Los Angeles, CA, USA"}]},{"given":"Chris","family":"Baker","sequence":"additional","affiliation":[{"name":"iSee, Inc., Cambridge, MA, USA"}]},{"given":"Yibiao","family":"Zhao","sequence":"additional","affiliation":[{"name":"iSee, Inc., Cambridge, MA, USA"}]},{"given":"Ying Nian","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California at Los Angeles, Los Angeles, CA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1535.003.0018"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.5220\/0001143902220229"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0005-1098(01)00174-1"},{"key":"ref4","first-page":"2635","article-title":"A theory of generative convnet","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Xie"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1201\/b10905-6"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1201\/b18312"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760128018"},{"key":"ref8","first-page":"695","article-title":"Estimation of non-normalized statistical models by score matching","volume":"6","author":"Hyv\u00e4rinen","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11834"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2879081"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3069023"},{"key":"ref12","first-page":"1433","article-title":"Maximum entropy inverse reinforcement learning","volume-title":"Proc. 23rd AAAI Conf. Artif. Intell. (AAAI)","volume":"8","author":"Ziebart"},{"issue":"2","key":"ref13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1023\/A:1007925832420","article-title":"Filters, random fields and maximum entropy (frame): Towards a unified theory for texture modeling","volume":"27","author":"Zhu","year":"1998","journal-title":"Int. J. Comput. Vis."},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-5833-1"},{"key":"ref15","article-title":"Maximum entropy deep inverse reinforcement learning","author":"Wulfmeier","year":"2015","journal-title":"arXiv:1507.04888"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2934852"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0757-x"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.119"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3045010"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00900"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01473"},{"issue":"10","key":"ref22","first-page":"1995","article-title":"Convolutional networks for images, speech, and time series","volume":"3361","author":"LeCun","year":"1995","journal-title":"The Handbook of Brain Theory and Neural Networks"},{"key":"ref23","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Krizhevsky"},{"key":"ref24","first-page":"49","article-title":"Guided cost learning: Deep inverse optimal control via policy optimization","volume-title":"Int. Conf. Mach. Learn. (ICML)","author":"Finn"},{"key":"ref25","article-title":"A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models","author":"Finn","year":"2016","journal-title":"arXiv:1611.03852"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3156\/jsoft.29.5_177_2"},{"key":"ref27","first-page":"4565","article-title":"Generative adversarial imitation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Ho"},{"key":"ref28","first-page":"3812","article-title":"InfoGAIL: Interpretable imitation learning from visual demonstrations","volume-title":"Adv. Neural Inf. Process. Syst. (NIPS)","author":"Li"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9674"},{"key":"ref30","first-page":"475","article-title":"Continuous inverse optimal control with locally optimal examples","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Levine"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460504"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2018.2804159"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01240"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17249"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17250"},{"article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kingma","key":"ref39"},{"article-title":"A tale of two flows: Cooperative learning of Langevin flow and normalizing flow toward energy-based model","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Xie","key":"ref40"},{"key":"ref41","article-title":"Glow: Generative flow with invertible $1\\times1$\n convolutions","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"31","author":"Kingma"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1002\/0471200611"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/57.1.97"},{"key":"ref45","first-page":"1683","article-title":"Stochastic gradient Hamiltonian Monte Carlo","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"32","author":"Chen"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5973"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015498"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995816"},{"key":"ref49","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"article-title":"U.s. highway 101 dataset","year":"2007","author":"Colyar","key":"ref50"},{"key":"ref51","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017","journal-title":"arXiv:1707.06347"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995721"},{"key":"ref54","first-page":"1889","article-title":"Trust region policy optimization","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Schulman"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593758"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793750"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71682-4_5"},{"article-title":"NICE: Non-linear independent components estimation","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR) Workshop","author":"Dinh","key":"ref58"},{"article-title":"Density estimation using real NVP","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Dinh","key":"ref59"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/5962385\/10336252\/9768861-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10336252\/09768861.pdf?arnumber=9768861","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T21:55:37Z","timestamp":1705960537000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9768861\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":59,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3168795","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2023,12]]}}}