{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T20:59:07Z","timestamp":1764277147673,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031059322"},{"type":"electronic","value":"9783031059339"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-05933-9_2","type":"book-chapter","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T13:04:50Z","timestamp":1652101490000},"page":"16-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Auxiliary Local Variables for\u00a0Improving Regularization\/Prior Approach in\u00a0Continual Learning"],"prefix":"10.1007","author":[{"given":"Linh Ngo","family":"Van","sequence":"first","affiliation":[]},{"given":"Nam Le","family":"Hai","sequence":"additional","affiliation":[]},{"given":"Hoang","family":"Pham","sequence":"additional","affiliation":[]},{"given":"Khoat","family":"Than","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"2_CR1","unstructured":"Ahn, H., Cha, S., Lee, D., Moon, T.: Uncertainty-based continual learning with adaptive regularization. In: Advances in Neural Information Processing Systems, pp. 4392\u20134402 (2019)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: learning what (not) to forget. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 139\u2013154 (2018)","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"2_CR3","unstructured":"Benzing, F.: Understanding regularisation methods for continual learning. In: Workshop of Advances in Neural Information Processing Systems (2020)"},{"key":"2_CR4","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: International Conference on Machine Learning, pp. 1613\u20131622. PMLR (2015)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"De Lange, M., et al.: A continual learning survey: defying forgetting in classification tasks. IEEE Trans. Pattern Anal. Mach. Intell. (2021)","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"2_CR6","unstructured":"Farquhar, S., Gal, Y.: A unifying Bayesian view of continual learning. In: The Bayesian Deep Learning Workshop at Neural Information Processing Systems (2018)"},{"key":"2_CR7","unstructured":"Gal, Y., Hron, J., Kendall, A.: Concrete dropout. In: Advances in Neural Information Processing Systems, pp. 3581\u20133590 (2017)"},{"key":"2_CR8","unstructured":"Ghahramani, Z., Attias, H.: Online variational Bayesian learning. In: Slides from talk Presented at NIPS Workshop on Online Learning (2000)"},{"key":"2_CR9","unstructured":"Goodfellow, I.J., Mirza, M., Xiao, D., Courville, A., Bengio, Y.: An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211 (2013)"},{"key":"2_CR10","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.ijar.2019.05.010","volume":"112","author":"C Ha","year":"2019","unstructured":"Ha, C., Tran, V.D., Van, L.N., Than, K.: Eliminating overfitting of probabilistic topic models on short and noisy text: the role of dropout. Int. J. Approximate Reasoning 112, 85\u2013104 (2019)","journal-title":"Int. J. Approximate Reasoning"},{"key":"2_CR11","unstructured":"Jung, S., Ahn, H., Cha, S., Moon, T.: Continual learning with node-importance based adaptive group sparse regularization. In: Advances in Neural Information Processing Systems (2020)"},{"key":"2_CR12","unstructured":"Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. In: Advances in Neural Information Processing Systems, vol. 28, pp. 2575\u20132583 (2015)"},{"issue":"13","key":"2_CR13","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"12","key":"2_CR14","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935\u20132947 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Dong, W., Zhang, L., Gong, D., Shi, Q.: Variational Bayesian dropout with a hierarchical prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7124\u20137133 (2019)","DOI":"10.1109\/CVPR.2019.00729"},{"key":"2_CR16","unstructured":"Loo, N., Swaroop, S., Turner, R.E.: Generalized variational continual learning. In: International Conference on Learning Representation (2021)"},{"key":"2_CR17","unstructured":"Mirzadeh, S., Farajtabar, M., Pascanu, R., Ghasemzadeh, H.: Understanding the role of training regimes in continual learning. In: Advances in Neural Information Processing Systems (2020)"},{"key":"2_CR18","unstructured":"Molchanov, D., Ashukha, A., Vetrov, D.: Variational dropout sparsifies deep neural networks. In: International Conference on Machine Learning, pp. 2498\u20132507 (2017)"},{"key":"2_CR19","unstructured":"Nguyen, C.V., Li, Y., Bui, T.D., Turner, R.E.: Variational continual learning. In: International Conference on Learning Representation (2018)"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Nguyen, V.S., Nguyen, D.T., Van, L.N., Than, K.: Infinite dropout for training Bayesian models from data streams. In: IEEE International Conference on Big Data (Big Data), pp. 125\u2013134. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9005544"},{"issue":"7","key":"2_CR21","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1162\/089976601750265045","volume":"13","author":"MA Sato","year":"2001","unstructured":"Sato, M.A.: Online model selection based on the variational Bayes. Neural Comput. 13(7), 1649\u20131681 (2001)","journal-title":"Neural Comput."},{"key":"2_CR22","unstructured":"Swaroop, S., Nguyen, C.V., Bui, T.D., Turner, R.E.: Improving and understanding variational continual learning. In: NeurIPS Continual Learning Workshop (2018)"},{"key":"2_CR23","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.neucom.2021.10.047","volume":"468","author":"N Van Linh","year":"2022","unstructured":"Van Linh, N., Bach, T.X., Than, K.: A graph convolutional topic model for short and noisy text streams. Neurocomputing 468, 345\u2013359 (2022)","journal-title":"Neurocomputing"},{"key":"2_CR24","unstructured":"Wei, C., Kakade, S.M., Ma, T.: The implicit and explicit regularization effects of dropout. In: Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 10181\u201310192. PMLR (2020)"},{"key":"2_CR25","unstructured":"Yin, D., Farajtabar, M., Li, A.: Sola: continual learning with second-order loss approximation. In: Workshop of Advances in Neural Information Processing Systems (2020)"},{"key":"2_CR26","first-page":"3987","volume":"70","author":"F Zenke","year":"2017","unstructured":"Zenke, F., Poole, B., Ganguli, S.: Continual learning through synaptic intelligence. Proc. Mach. Learn. Res. 70, 3987 (2017)","journal-title":"Proc. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-05933-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T01:06:48Z","timestamp":1654132008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-05933-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031059322","9783031059339"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-05933-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"16 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/pakdd.net\/index.html","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"558","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":"121","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":"22% - 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":"3.75","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":"6.45","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)"}}]}}