{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:42:37Z","timestamp":1776490957058,"version":"3.51.2"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"U.S. Government"},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["HR0011-22-9-0114"],"award-info":[{"award-number":["HR0011-22-9-0114"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006353","name":"Draper","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006353","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Draper Scholars program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/lra.2024.3511376","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T19:19:21Z","timestamp":1733339961000},"page":"660-667","source":"Crossref","is-referenced-by-count":2,"title":["Deep Modeling of Non-Gaussian Aleatoric Uncertainty"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6523-8739","authenticated-orcid":false,"given":"Aastha","family":"Acharya","sequence":"first","affiliation":[{"name":"Charles Stark Draper Laboratory, Inc., Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5815-3023","authenticated-orcid":false,"given":"Caleb","family":"Lee","sequence":"additional","affiliation":[{"name":"Charles Stark Draper Laboratory, Inc., Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marissa","family":"D'Alonzo","sequence":"additional","affiliation":[{"name":"Charles Stark Draper Laboratory, Inc., Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7991-6454","authenticated-orcid":false,"given":"Jared","family":"Shamwell","sequence":"additional","affiliation":[{"name":"Charles Stark Draper Laboratory, Inc., Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7555-5671","authenticated-orcid":false,"given":"Nisar R.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Ann and H. J. Smead Department of Aerospace Engineering Sciences, University of Colorado, Boulder, Boulder, CO, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4012-4513","authenticated-orcid":false,"given":"Rebecca","family":"Russell","sequence":"additional","affiliation":[{"name":"Charles Stark Draper Laboratory, Inc., Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/02783640122067435"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.strusafe.2008.06.020"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1115\/1.3662552"},{"key":"ref4","first-page":"1853","article-title":"A systematic approach for Kalman-type filtering with non-Gaussian noises","volume-title":"Proc. IEEE 19th Int. Conf. Inf. Fusion","author":"Raitoharju","year":"2016"},{"key":"ref5","article-title":"Neural density estimation and likelihood-free inference","author":"Papamakarios","year":"2019"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1049\/ip-f-2.1993.0015"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2013.6630699"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2006.1638022"},{"key":"ref9","first-page":"1","article-title":"A decentralized kernel density estimation approach to distributed robot path planning","volume-title":"Proc. Neural Inf. Process. Syst. Conf.","author":"Foderaro","year":"2012"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S0262-8856(00)00086-X"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1999.10473832"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/83.1.189"},{"key":"ref13","first-page":"781","article-title":"Conditional density estimation via least-squares density ratio estimation","volume-title":"Proc. 13th Int. Conf. Artif. Intell. And Statist.","author":"Sugiyama","year":"2010"},{"key":"ref14","article-title":"Conditional density estimation with neural networks: Best practices and benchmarks","author":"Rothfuss","year":"2019"},{"key":"ref15","article-title":"A comparison of uncertainty estimation approaches in deep learning components for autonomous vehicle applications","volume-title":"Proc. Workshop Artif. Intell. Saf.","author":"Arnez","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3086757"},{"key":"ref18","article-title":"Mixture density networks","author":"Bishop","year":"1994"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1002\/SERIES1345"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20567"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2021.107203"},{"key":"ref22","article-title":"Better conditional density estimation for neural networks","author":"Tansey","year":"2016"},{"key":"ref23","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma","year":"2014"},{"key":"ref24","article-title":"Density estimation using real NVP","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dinh","year":"2017"},{"key":"ref25","first-page":"2335","article-title":"Masked autoregressive flow for density estimation","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Papamakarios","year":"2017"},{"key":"ref26","first-page":"4743","article-title":"Improved variational inference with inverse autoregressive flow","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kingma","year":"2016"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981991"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160766"},{"issue":"57","key":"ref29","first-page":"1","article-title":"Normalizing flows for probabilistic modeling and inference","volume":"22","author":"Papamakarios","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref30","volume-title":"Information Theory, Inference, and Learning Algorithms","author":"MacKay","year":"2003"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176343842"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.44.12.1650"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.2514\/6.2017-3723"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.2514\/6.2020-0370"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.2514\/6.2023-0875"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7083369\/10768868\/10777050.pdf?arnumber=10777050","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T06:26:24Z","timestamp":1734071184000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10777050\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":35,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/lra.2024.3511376","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}