{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:31:49Z","timestamp":1742949109464,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031301070"},{"type":"electronic","value":"9783031301087"}],"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\/978-3-031-30108-7_10","type":"book-chapter","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T04:03:04Z","timestamp":1681272184000},"page":"113-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning Associative Reasoning Towards Systematicity Using Modular Networks"],"prefix":"10.1007","author":[{"given":"Jun-Hyun","family":"Bae","sequence":"first","affiliation":[]},{"given":"Taewon","family":"Park","sequence":"additional","affiliation":[]},{"given":"Minho","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"10_CR1","unstructured":"Banino, A., et al.: Memo: a deep network for flexible combination of episodic memories. In: International Conference on Learning Representations (2020)"},{"key":"10_CR2","unstructured":"Csord\u00e1s, R., Schmidhuber, J.: Improving differentiable neural computers through memory masking, de-allocation, and link distribution sharpness control. In: International Conference on Learning Representations (2019)"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860 (2019)","DOI":"10.18653\/v1\/P19-1285"},{"issue":"1\u20132","key":"10_CR4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0010-0277(88)90031-5","volume":"28","author":"JA Fodor","year":"1988","unstructured":"Fodor, J.A., Pylyshyn, Z.W.: Connectionism and cognitive architecture: a critical analysis. Cognition 28(1\u20132), 3\u201371 (1988)","journal-title":"Cognition"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Franke, J., Niehues, J., Waibel, A.: Robust and scalable differentiable neural computer for question answering. In: Proceedings of the Workshop on Machine Reading for Question Answering, pp. 47\u201359 (2018)","DOI":"10.18653\/v1\/W18-2606"},{"key":"10_CR6","unstructured":"Goyal, A., et al.: Recurrent independent mechanisms. arXiv preprint arXiv:1909.10893 (2019)"},{"issue":"7626","key":"10_CR7","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1038\/nature20101","volume":"538","author":"A Graves","year":"2016","unstructured":"Graves, A., et al.: Hybrid computing using a neural network with dynamic external memory. Nature 538(7626), 471 (2016)","journal-title":"Nature"},{"issue":"8","key":"10_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1613\/jair.1.11674","volume":"67","author":"D Hupkes","year":"2020","unstructured":"Hupkes, D., Dankers, V., Mul, M., Bruni, E.: Compositionality decomposed: how do neural networks generalise? J. Artif. Intell. Res. 67, 757\u2013795 (2020)","journal-title":"J. Artif. Intell. Res."},{"key":"10_CR10","unstructured":"Le, H., Tran, T., Venkatesh, S.: Self-attentive associative memory. In: International Conference on Machine Learning, pp. 5682\u20135691. PMLR (2020)"},{"key":"10_CR11","unstructured":"Madan, K., Ke, N.R., Goyal, A., Sch\u00f6lkopf, B., Bengio, Y.: Fast and slow learning of recurrent independent mechanisms. arXiv preprint arXiv:2105.08710 (2021)"},{"key":"10_CR12","unstructured":"Munkhdalai, T., Sordoni, A., Wang, T., Trischler, A.: Metalearned neural memory. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"10_CR13","unstructured":"Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., Sch\u00f6lkopf, B.: Learning independent causal mechanisms. In: International Conference on Machine Learning, pp. 4036\u20134044. PMLR (2018)"},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neunet.2021.07.030","volume":"144","author":"T Park","year":"2021","unstructured":"Park, T., Choi, I., Lee, M.: Distributed associative memory network with memory refreshing loss. Neural Netw. 144, 33\u201348 (2021)","journal-title":"Neural Netw."},{"key":"10_CR15","unstructured":"Rae, J.W., et al.: Scaling memory-augmented neural networks with sparse reads and writes. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3628\u20133636 (2016)"},{"key":"10_CR16","unstructured":"Schlag, I., Munkhdalai, T., Schmidhuber, J.: Learning associative inference using fast weight memory. arXiv preprint arXiv:2011.07831 (2020)"},{"key":"10_CR17","unstructured":"Schlag, I., Schmidhuber, J.: Learning to reason with third order tensor products. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"1\u20132","key":"10_CR18","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/0004-3702(90)90007-M","volume":"46","author":"P Smolensky","year":"1990","unstructured":"Smolensky, P.: Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artif. Intell. 46(1\u20132), 159\u2013216 (1990)","journal-title":"Artif. Intell."},{"key":"10_CR19","unstructured":"Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, pp. 2440\u20132448 (2015)"},{"key":"10_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"10_CR21","unstructured":"Weston, J., et al.: Towards AI-complete question answering: a set of prerequisite toy tasks. arXiv e-prints pp. arXiv-1502 (2015)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30108-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T04:04:31Z","timestamp":1681272271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30108-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031301070","9783031301087"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30108-7_10","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":"13 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"44% - 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":"2.65","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":"3","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)"}},{"value":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}