{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:00:04Z","timestamp":1742965204437,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031251979"},{"type":"electronic","value":"9783031251986"}],"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\/978-3-031-25198-6_35","type":"book-chapter","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T07:32:38Z","timestamp":1676014358000},"page":"474-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Task-Aware Attention-Based Method for\u00a0Improved Meta-Learning"],"prefix":"10.1007","author":[{"given":"Yue","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xinxing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yalin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Qitao","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Longfei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,10]]},"reference":[{"key":"35_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) ICLR 2015 (2015)"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Dong, C., Li, W., Huo, J., Gu, Z., Gao, Y.: Learning task-aware local representations for few-shot learning. In: Bessiere, C. (ed.) IJCAI, pp. 716\u2013722 (2020)","DOI":"10.24963\/ijcai.2020\/100"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Dong, M., Yuan, F., Yao, L., Xu, X., Zhu, L.: MAMO: memory-augmented meta-optimization for cold-start recommendation. In: KDD 2020. ACM (2020)","DOI":"10.1145\/3394486.3403113"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: CVPR 2019, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Generating classification weights with GNN denoising autoencoders for few-shot learning. In: CVPR 2019, pp. 21\u201330 (2019)","DOI":"10.1109\/CVPR.2019.00011"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1\u201319:19 (2016)","DOI":"10.1145\/2827872"},{"key":"35_CR7","unstructured":"Hou, R., Chang, H., Ma, B., Shan, S., Chen, X.: Cross attention network for few-shot classification. In: NeurIPS 2019, pp. 4005\u20134016 (2019)"},{"key":"35_CR8","doi-asserted-by":"crossref","unstructured":"Lee, H., I.m, J., Jang, S., Cho, H., Chung, S.: MELU: meta-learned user preference estimator for cold-start recommendation. In: SIGKDD 2019, pp. 1073\u20131082 (2019)","DOI":"10.1145\/3292500.3330859"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: CVPR 2019, pp. 10657\u201310665 (2019)","DOI":"10.1109\/CVPR.2019.01091"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Li, H., Eigen, D., Dodge, S., Zeiler, M., Wang, X.: Finding task-relevant features for few-shot learning by category traversal. In: CVPR 2019, pp. 1\u201310 (2019)","DOI":"10.1109\/CVPR.2019.00009"},{"key":"35_CR11","unstructured":"Li, Z., Zhou, F., Chen, F., Li, H.: Meta-SGD: learning to learn quickly for few shot learning. CoRR abs\/1707.09835 (2017)"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Liu, C., Xu, C., Wang, Y., Zhang, L., Fu, Y.: An embarrassingly simple baseline to one-shot learning. In: CVPR Workshops 2020, pp. 4005\u20134009. IEEE (2020)","DOI":"10.1109\/CVPRW50498.2020.00469"},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Lu, Y., Fang, Y., Shi, C.: Meta-learning on heterogeneous information networks for cold-start recommendation. In: KDD 2020, pp. 1563\u20131573. ACM (2020)","DOI":"10.1145\/3394486.3403207"},{"key":"35_CR14","unstructured":"Oreshkin, B.N., L\u00f3pez, P.R., Lacoste, A.: TADAM: task dependent adaptive metric for improved few-shot learning. In: NeurIPS 2018, pp. 719\u2013729 (2018)"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: CVPR 2018, pp. 7229\u20137238 (2018)","DOI":"10.1109\/CVPR.2018.00755"},{"key":"35_CR16","unstructured":"Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. In: ICLR 2018 (2018)"},{"key":"35_CR17","unstructured":"Rusu, A.A., et al.: Meta-learning with latent embedding optimization. In: ICLR 2019 (2019)"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., Zhang, C.: DiSAN: directional self-attention network for RNN\/CNN-free language understanding. In: AAAI 2018 (2018)","DOI":"10.1609\/aaai.v32i1.11941"},{"key":"35_CR19","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: NeurIPS 2017, pp. 4077\u20134087 (2017)"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR 2018 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"35_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)"},{"key":"35_CR22","unstructured":"Wang, Y., Chao, W., Weinberger, K.Q., van der Maaten, L.: Simpleshot: evisiting Nearest-neighbor classification For Few-shot Learning. CoRR (2019)"},{"key":"35_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-95408-6_11","volume-title":"Advanced Data Mining and Applications","author":"J Wu","year":"2022","unstructured":"Wu, J., Li, B., Ji, Y., Tian, J., Xiang, Y.: Text-enhanced knowledge graph representation model in hyperbolic space. In: Li, B., et al. (eds.) ADMA 2022. LNCS (LNAI), vol. 13088, pp. 137\u2013149. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-95408-6_11"},{"key":"35_CR24","doi-asserted-by":"publisher","unstructured":"Zang, Y., et al.: GISDCN: a graph-based interpolation sequential recommender with deformable convolutional network. In: DASFAA, pp. 289\u2013297. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-00126-0_21","DOI":"10.1007\/978-3-031-00126-0_21"},{"key":"35_CR25","doi-asserted-by":"publisher","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25198-6_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T08:03:53Z","timestamp":1676016233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25198-6_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031251979","9783031251986"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25198-6_35","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 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"11 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb-waim2022.com\/proceedings","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":"297","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":"75","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":"45","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":"25% - 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","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":"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)"}},{"value":"5 Demo papers + 23 workshop 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)"}}]}}