iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.crossref.org/works/10.1109/TCBB.2021.3110516
{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T21:10:57Z","timestamp":1722460257363},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"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":["IEEE\/ACM Trans. Comput. Biol. and Bioinf."],"published-print":{"date-parts":[[2022,11,1]]},"DOI":"10.1109\/tcbb.2021.3110516","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T20:02:15Z","timestamp":1631131335000},"page":"3553-3563","source":"Crossref","is-referenced-by-count":2,"title":["LipGene: Lipschitz Continuity Guided Adaptive Learning Rates for Fast Convergence on Microarray Expression Data Sets"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7594-1142","authenticated-orcid":false,"given":"Tejas","family":"Prashanth","sequence":"first","affiliation":[{"name":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"}]},{"given":"Snehanshu","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Information Systems and APPCAIR, BITS Pilani K K Birla Goa Campus, Goa, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6456-787X","authenticated-orcid":false,"given":"Sumedh","family":"Basarkod","sequence":"additional","affiliation":[{"name":"Center for AstroInformatics, Modeling and Simulation, Bangalore, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7930-4116","authenticated-orcid":false,"given":"Suraj","family":"Aralihalli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"}]},{"given":"Soma S.","family":"Dhavala","sequence":"additional","affiliation":[{"name":"MLSquare, Bangalore, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5494-9391","authenticated-orcid":false,"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Indian Institute of Technology Patna, Bihar, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9627-4986","authenticated-orcid":false,"given":"Raviprasad","family":"Aduri","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, BITS Pilani, K K Birla Goa campus, Goa, India"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref39","DOI":"10.1109\/IJCNN48605.2020.9207083"},{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.1109\/IJCNN48605.2020.9207650"},{"key":"ref33","first-page":"3835","article-title":"Lipschitz regularity of deep neural networks: Analysis and efficient estimation","author":"virmaux","year":"2018","journal-title":"Proc 32nd Int Conf Neural Inf Process Syst"},{"year":"0","author":"krishnan","journal-title":"Adv Neural Inform Process Syst","article-title":"Lipschitz bounds and provably robust training by laplacian smoothing","key":"ref32"},{"key":"ref31","first-page":"8588","article-title":"A closer look at accuracy vs. robustness","author":"yang","year":"2020","journal-title":"Adv Neural Inform Process Syst"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1007\/s10489-020-01892-0"},{"doi-asserted-by":"publisher","key":"ref37","DOI":"10.1109\/IJCNN.1999.832644"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0893-6080(96)00052-4","article-title":"Effective backpropagation training with variable stepsize","volume":"10","author":"androulakis","year":"1997","journal-title":"Neural Netw"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.2140\/pjm.1966.16.1"},{"key":"ref34","first-page":"181","article-title":"Deep neural networks for estimation and inference: Application to causal effects and other semiparametric estimands","volume":"89","author":"farrell","year":"0"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1007\/978-3-030-28584-5_11"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1038\/cgt.2016.63"},{"year":"2015","author":"hardt","journal-title":"CoRR","article-title":"Train faster, generalize better: Stability of stochastic gradient descent","key":"ref40"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1089\/106652799318274"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1186\/1471-2164-9-S1-S13"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1093\/bioinformatics\/btg1071"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1145\/3219819.3220114"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1007\/978-3-642-61068-4_7"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref18","first-page":"1020","article-title":"Towards flatter loss surface via nonmonotonic learning rate scheduling","author":"seong","year":"2018","journal-title":"Proc Conf Uncertainty of Artificial Intelligence"},{"year":"0","author":"kingma","article-title":"Adam: A method for stochastic optimization","key":"ref19"},{"year":"2017","author":"shrikumar","first-page":"3145","article-title":"Learning important features through propagating activation differences","key":"ref28"},{"key":"ref4","first-page":"6417","article-title":"Single-model uncertainties for deep learning","author":"tagasovska","year":"2019","journal-title":"Proc 33rd Int Conf Neural Inf Process Syst"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1038\/s42256-019-0048-x"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"year":"2020","author":"sridhar","first-page":"1","article-title":"Parsimonious computing: A minority training regime for effective prediction in large microarray expression data sets","key":"ref6"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1609\/aaai.v31i1.10894"},{"year":"2016","author":"sundararajan","article-title":"Gradients of counterfactuals","key":"ref29"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"van 't veer","year":"2002","journal-title":"Nature"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/IJCNN48605.2020.9207650"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1093\/bioinformatics\/btw074","article-title":"Gene expression inference with deep learning","volume":"32","author":"chen","year":"2016","journal-title":"Bioinformatics"},{"key":"ref9","article-title":"Gene expression: An overview of methods and applications for cancer research","volume":"23","author":"monobe","year":"2016","journal-title":"Veterinaria e Zootecnia"},{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1145\/3219819.3220114"},{"key":"ref20","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2011","journal-title":"J Mach Learn Res"},{"doi-asserted-by":"publisher","key":"ref46","DOI":"10.3389\/fgene.2018.00535"},{"doi-asserted-by":"publisher","key":"ref45","DOI":"10.1038\/nature12912"},{"key":"ref22","first-page":"1231","article-title":"Nonparametric quantile estimation","volume":"7","author":"takeuchi","year":"2006","journal-title":"J Mach Learn Res"},{"doi-asserted-by":"publisher","key":"ref48","DOI":"10.1126\/science.aaa0355"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.2307\/1390613"},{"doi-asserted-by":"publisher","key":"ref47","DOI":"10.1038\/s41598-019-43881-5"},{"year":"2019","author":"nar","article-title":"Cross-entropy loss leads to poor margins","key":"ref24"},{"doi-asserted-by":"publisher","key":"ref42","DOI":"10.1038\/nature12531"},{"doi-asserted-by":"publisher","key":"ref23","DOI":"10.1073\/pnas.1810420116"},{"doi-asserted-by":"publisher","key":"ref41","DOI":"10.1126\/science.1262110"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/CVPR.2015.7298640"},{"year":"2018","author":"reddi","journal-title":"Proc Int Conf Learn Representations","article-title":"On the convergence of adam and beyond","key":"ref44"},{"year":"2019","author":"torrent\u00e9","journal-title":"BioRxiv","article-title":"The shape of gene expression distributions matter: How incorporating distribution shape improves the interpretation of cancer transcriptomic data","key":"ref25"},{"year":"2012","author":"zeiler","journal-title":"CoRR","article-title":"ADADELTA: An adaptive learning rate method","key":"ref43"}],"container-title":["IEEE\/ACM Transactions on Computational Biology and Bioinformatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8857\/9976468\/09531348.pdf?arnumber=9531348","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T20:28:44Z","timestamp":1722457724000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9531348\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,1]]},"references-count":48,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tcbb.2021.3110516","relation":{},"ISSN":["1545-5963","1557-9964","2374-0043"],"issn-type":[{"type":"print","value":"1545-5963"},{"type":"electronic","value":"1557-9964"},{"type":"electronic","value":"2374-0043"}],"subject":[],"published":{"date-parts":[[2022,11,1]]}}}