{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:12:41Z","timestamp":1743131561735,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031258909"},{"type":"electronic","value":"9783031258916"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25891-6_27","type":"book-chapter","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T14:03:34Z","timestamp":1678370614000},"page":"357-372","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preconditioned Gradient Method for Data Approximation with Shallow Neural Networks"],"prefix":"10.1007","author":[{"given":"Nadja","family":"Vater","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8050-1336","authenticated-orcid":false,"given":"Alfio","family":"Borz\u00ec","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"issue":"3","key":"27_CR1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1093\/imamat\/12.3.223","volume":"12","author":"CG Broyden","year":"1973","unstructured":"Broyden, C.G., Dennis, J.E., Jr., Mor\u00e9, J.J.: On the local and superlinear convergence of quasi-Newton methods. IMA J. Appl. Math. 12(3), 223\u2013245 (1973)","journal-title":"IMA J. Appl. Math."},{"key":"27_CR2","unstructured":"Crane, R., Roosta, F.: Invexifying regularization of non-linear least-squares problems. arXiv preprint arXiv:2111.11027 (2021)"},{"issue":"7","key":"27_CR3","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(7), 2121\u20132159 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"27_CR4","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"27_CR5","unstructured":"Gorbunov, E., Hanzely, F., Richt\u00e1rik, P.: A unified theory of SGD: variance reduction, sampling, quantization and coordinate descent. In: International Conference on Artificial Intelligence and Statistics, pp. 680\u2013690. PMLR (2020)"},{"issue":"2","key":"27_CR6","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1137\/090771806","volume":"53","author":"N Halko","year":"2011","unstructured":"Halko, N., Martinsson, P.G., Tropp, J.A.: Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev. 53(2), 217\u2013288 (2011)","journal-title":"SIAM Rev."},{"key":"27_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-8351-9020-7","volume-title":"Grundlagen der numerischen Mathematik und des wissenschaftlichen Rechnens","author":"M Hanke-Bourgeois","year":"2009","unstructured":"Hanke-Bourgeois, M.: Grundlagen der numerischen Mathematik und des wissenschaftlichen Rechnens, 3rd edn. Vieweg + Teubner, Wiesbaden (2009). https:\/\/doi.org\/10.1007\/978-3-8351-9020-7","edition":"3"},{"issue":"4","key":"27_CR8","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1093\/imamat\/25.4.361","volume":"25","author":"GT Herman","year":"1980","unstructured":"Herman, G.T., Lent, A., Hurwitz, H.: A storage-efficient algorithm for finding the regularized solution of a large, inconsistent system of equations. IMA J. Appl. Math. 25(4), 361\u2013366 (1980)","journal-title":"IMA J. Appl. Math."},{"key":"27_CR9","unstructured":"Lange, S., Helfrich, K., Ye, Q.: Batch normalization preconditioning for neural network training. arXiv preprint arXiv:2108.01110 (2021)"},{"issue":"2","key":"27_CR10","doi-asserted-by":"publisher","first-page":"C95","DOI":"10.1137\/120866580","volume":"36","author":"X Meng","year":"2014","unstructured":"Meng, X., Saunders, M.A., Mahoney, M.W.: LSRN: a parallel iterative solver for strongly over-or underdetermined systems. SIAM J. Sci. Comput. 36(2), C95\u2013C118 (2014)","journal-title":"SIAM J. Sci. Comput."},{"key":"27_CR11","unstructured":"Onose, A., Mossavat, S.I., Smilde, H.J.H.: A preconditioned accelerated stochastic gradient descent algorithm. In: 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2020)"},{"issue":"2","key":"27_CR12","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1145\/355993.356000","volume":"8","author":"CC Paige","year":"1982","unstructured":"Paige, C.C., Saunders, M.A.: Algorithm 583: LSQR: sparse linear equations and least squares problems. ACM Trans. Math. Softw. 8(2), 195\u2013209 (1982)","journal-title":"ACM Trans. Math. Softw."},{"key":"27_CR13","unstructured":"Prechelt, L.: Proben1: a set of neural network benchmark problems and benchmarking rules (1994)"},{"issue":"10","key":"27_CR14","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1109\/TMI.2019.2897943","volume":"38","author":"Y Qiao","year":"2019","unstructured":"Qiao, Y., Lelieveldt, B.P., Staring, M.: An efficient preconditioner for stochastic gradient descent optimization of image registration. IEEE Trans. Med. Imaging 38(10), 2314\u20132325 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"27_CR15","doi-asserted-by":"publisher","unstructured":"Vater, N., Borz\u00ec, A.: Training artificial neural networks with gradient and coarse-level correction schemes. In: Nicosia, G., et al. (eds.) International Conference on Machine Learning, Optimization, and Data Science, pp. 473\u2013487. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-95467-3_34","DOI":"10.1007\/978-3-030-95467-3_34"},{"key":"27_CR16","unstructured":"Zhang, J., Fattahi, S., Zhang, R.: Preconditioned gradient descent for over-parameterized nonconvex matrix factorization. Adv. Neural Inf. Process. Syst. 34 (2021)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25891-6_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T10:19:48Z","timestamp":1680689988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25891-6_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031258909","9783031258916"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25891-6_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Certosa di Pontignano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lod2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2022.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}