{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:03:34Z","timestamp":1764403414371,"version":"3.28.2"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,22]]},"DOI":"10.1109\/mlsp58920.2024.10734787","type":"proceedings-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T18:31:55Z","timestamp":1730745115000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design"],"prefix":"10.1109","author":[{"given":"A N M","family":"Nafiz Abeer","sequence":"first","affiliation":[{"name":"Texas A&M University,Department of Electrical and Computer Engineering,College Station,TX,USA"}]},{"given":"Sanket","family":"Jantre","sequence":"additional","affiliation":[{"name":"Computational Science Initiative, Brookhaven National Laboratory,Upton,NY,USA"}]},{"given":"Nathan M","family":"Urban","sequence":"additional","affiliation":[{"name":"Computational Science Initiative, Brookhaven National Laboratory,Upton,NY,USA"}]},{"given":"Byung-Jun","family":"Yoon","sequence":"additional","affiliation":[{"name":"Texas A&M University,Department of Electrical and Computer Engineering,College Station,TX,USA"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Sample efficiency matters: a benchmark for practical molecular optimization","author":"Gao","year":"2022","journal-title":"NeurIPS"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"ref3","article-title":"Junction tree variational autoencoder for molecular graph generation","author":"Jin","year":"2018","journal-title":"ICML"},{"key":"ref4","article-title":"Multi-objective molecule generation using interpretable substructures","author":"Jin","year":"2020","journal-title":"ICML"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0235-x"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-47148-x"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00502"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00975"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-023-00682-3"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1039\/d4dd00088a"},{"key":"ref11","article-title":"Improving black-box optimization in vae latent space using decoder uncertainty","author":"Notin","year":"2021","journal-title":"NeurIPS"},{"key":"ref12","article-title":"Bayesian model comparison and backprop nets","author":"MacKay","year":"1991","journal-title":"NIPS"},{"key":"ref13","article-title":"What are bayesian neural network posteriors really like?","author":"Izmailov","year":"2021","journal-title":"ICML"},{"journal-title":"Rethinking parameter counting in deep models: Effective dimensionality revisited","year":"2020","author":"Maddox","key":"ref14"},{"key":"ref15","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","volume-title":"ICLR","author":"Frankle","year":"2019"},{"key":"ref16","article-title":"Weight uncertainty in neural networks","author":"Blundell","year":"2015","journal-title":"ICML"},{"key":"ref17","article-title":"Bayesian compression for deep learning","author":"Louizos","year":"2017","journal-title":"NIPS"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.08.029"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2024.3485529\/mm1"},{"key":"ref20","article-title":"Subspace inference for Bayesian deep learning","author":"Izmailov","year":"2020","journal-title":"UAI"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10448265"},{"key":"ref22","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"Gal","year":"2016","journal-title":"ICML"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref24","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015","journal-title":"ICLR"},{"key":"ref25","article-title":"Sample-efficient optimization in the latent space of deep generative models via weighted retraining","author":"Tripp","year":"2020","journal-title":"NeurIPS"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2008.07.004"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/nature25758"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0287-6"},{"issue":"1","key":"ref29","article-title":"Natural product-likeness score and its application for prioritization of compound libraries","volume":"48","author":"Ertl","year":"2007","journal-title":"J. Cherm. Inf. Model."},{"key":"ref30","article-title":"Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling","volume":"8","author":"Landrum","year":"2013","journal-title":"Greg Landrum"},{"key":"ref31","article-title":"Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development","author":"Huang","year":"2021","journal-title":"NeurIPS Datasets and Benchmarks"},{"journal-title":"Multi-objective latent space optimization of generative molecular design models","year":"2022","author":"Abeer","key":"ref32"}],"event":{"name":"2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)","start":{"date-parts":[[2024,9,22]]},"location":"London, United Kingdom","end":{"date-parts":[[2024,9,25]]}},"container-title":["2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10734669\/10734712\/10734787.pdf?arnumber=10734787","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T03:58:08Z","timestamp":1732679888000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10734787\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,22]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/mlsp58920.2024.10734787","relation":{},"subject":[],"published":{"date-parts":[[2024,9,22]]}}}