{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T04:48:34Z","timestamp":1777783714712,"version":"3.51.4"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100006132","name":"Office of Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006132","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,14]]},"DOI":"10.1109\/icassp48485.2024.10448265","type":"proceedings-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T18:56:31Z","timestamp":1710788191000},"page":"5330-5334","source":"Crossref","is-referenced-by-count":6,"title":["Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks"],"prefix":"10.1109","author":[{"given":"Sanket","family":"Jantre","sequence":"first","affiliation":[{"name":"Brookhaven National Laboratory,Computational Science Initiative,Upton,NY"}]},{"given":"Nathan M.","family":"Urban","sequence":"additional","affiliation":[{"name":"Brookhaven National Laboratory,Computational Science Initiative,Upton,NY"}]},{"given":"Xiaoning","family":"Qian","sequence":"additional","affiliation":[{"name":"Brookhaven National Laboratory,Computational Science Initiative,Upton,NY"}]},{"given":"Byung-Jun","family":"Yoon","sequence":"additional","affiliation":[{"name":"Brookhaven National Laboratory,Computational Science Initiative,Upton,NY"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref2","article-title":"Concrete problems in AI safety","author":"Amodei","year":"2016"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/nature14541"},{"key":"ref4","article-title":"Bayesian inference with certifiable adversarial robustness","volume-title":"AISTATS","author":"Wicker"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007665907178"},{"key":"ref6","article-title":"What are Bayesian neural network posteriors really like?","volume-title":"ICML","author":"Izmailov"},{"key":"ref7","article-title":"Subspace inference for Bayesian deep learning","volume-title":"UAI","author":"Izmailov"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973860"},{"key":"ref9","article-title":"Bayesian model comparison and backprop nets","volume-title":"NIPS","author":"MacKay"},{"key":"ref10","article-title":"Rethinking parameter counting in deep models: Effective dimensionality revisited","author":"Maddox","year":"2020"},{"key":"ref11","article-title":"A simple baseline for Bayesian uncertainty in deep learning","author":"Maddox","year":"2019","journal-title":"NeurIPS"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1137\/130916138"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2015.09.001"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2017040"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1137\/19M1296070"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1115\/DETC2019-98099"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref18","article-title":"Auto-encoding variational Bayes","volume-title":"ICLR","author":"Kingma"},{"key":"ref19","article-title":"Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective","author":"Baptista","year":"2022"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.114199"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1137\/18M1221837"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1088\/0266-5611\/30\/11\/114015"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3150\/21-BEJ1437"},{"key":"ref24","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019","journal-title":"NeurIPS"},{"key":"ref25","article-title":"Weight uncertainty in neural network","volume-title":"ICML","author":"Blundell"},{"key":"ref26","article-title":"Adam: A method for stochastic optimization","volume-title":"ICLR","author":"Kingma"},{"issue":"2","key":"ref27","article-title":"Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme","volume":"2","author":"Melland","year":"2021","journal-title":"Machine Learning: Science and Technology"}],"event":{"name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Seoul, Korea, Republic of","start":{"date-parts":[[2024,4,14]]},"end":{"date-parts":[[2024,4,19]]}},"container-title":["ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10445798\/10445803\/10448265.pdf?arnumber=10448265","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T04:51:24Z","timestamp":1722660684000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10448265\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/icassp48485.2024.10448265","relation":{},"subject":[],"published":{"date-parts":[[2024,4,14]]}}}