{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:14:05Z","timestamp":1750220045686,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Strategic Research and Consulting Project of the Chinese Academy of Engineering","award":["2022-XY-107"],"award-info":[{"award-number":["2022-XY-107"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan Project","award":["22511100700"],"award-info":[{"award-number":["22511100700"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,2,17]]},"DOI":"10.1145\/3587716.3587803","type":"proceedings-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T23:27:30Z","timestamp":1694129250000},"page":"528-533","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Candidate Batch Mode Active Learning Approach"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9776-0047","authenticated-orcid":false,"given":"Xinyu","family":"Wang","sequence":"first","affiliation":[{"name":"Tongji University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7185-9731","authenticated-orcid":false,"given":"Junlii","family":"Wang","sequence":"additional","affiliation":[{"name":"Tongji University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1917-9616","authenticated-orcid":false,"given":"Chungang","family":"Yan","sequence":"additional","affiliation":[{"name":"Tongji University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"volume-title":"Artificial intelligence, explanations, trust, responsibility, and justice","author":"Baecker R. M.","key":"e_1_3_2_1_1_1","unstructured":"Baecker, R. M. Artificial intelligence, explanations, trust, responsibility, and justice. In Computers and Society. Oxford University Press."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_3_1","volume-title":"Active Learning for Hyperspectral Image Classification: A Comparative Review.\u00a0IEEE geoscience and remote sensing magazine, 2-24","author":"Thoreau R.","year":"2022","unstructured":"Thoreau, R., Achard, V., Risser, L., Berthelot, B., & Briottet, X. (2022). Active Learning for Hyperspectral Image Classification: A Comparative Review.\u00a0IEEE geoscience and remote sensing magazine, 2-24."},{"key":"e_1_3_2_1_4_1","volume-title":"Problem solving and rule induction: A unified view. Knowledge and cognition, 105-127","author":"Simon H. A.","year":"1974","unstructured":"Simon, H. A., & Lea, G. (1974). Problem solving and rule induction: A unified view. Knowledge and cognition, 105-127."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143897"},{"key":"e_1_3_2_1_6_1","volume-title":"A survey of deep active learning. ACM computing surveys (CSUR), 54(9), 1-40","author":"Ren P.","year":"2021","unstructured":"Ren, P., Xiao, Y., Chang, X., Huang, P. Y., Li, Z., Gupta, B. B., ... & Wang, X. (2021). A survey of deep active learning. ACM computing surveys (CSUR), 54(9), 1-40."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00807"},{"key":"e_1_3_2_1_8_1","volume-title":"In\u00a0Asian Conference on Computer Vision\u00a0(pp. 506-522)","author":"Kao C. C.","year":"2018","unstructured":"Kao, C. C., Lee, T. Y., Sen, P., & Liu, M. Y. (2018, December). Localization-aware active learning for object detection. In\u00a0Asian Conference on Computer Vision\u00a0(pp. 506-522). Springer, Cham."},{"key":"e_1_3_2_1_9_1","volume-title":"How to measure uncertainty in uncertainty sampling for active learning.\u00a0Machine Learning ,\u00a0111 (1), 89-122","author":"Nguyen V. L.","year":"2022","unstructured":"Nguyen, V. L., Shaker, M. H., & H\u00fcllermeier, E. (2022). How to measure uncertainty in uncertainty sampling for active learning.\u00a0Machine Learning ,\u00a0111 (1), 89-122."},{"key":"e_1_3_2_1_10_1","volume-title":"International conference on machine learning (pp. 1613-1622)","author":"Blundell C.","year":"2015","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015, June). Weight uncertainty in neural network. In International conference on machine learning (pp. 1613-1622). PMLR."},{"key":"e_1_3_2_1_11_1","volume-title":"international conference on machine learning (pp. 1050-1059)","author":"Gal Y.","year":"2016","unstructured":"Gal, Y., & Ghahramani, Z. (2016, June). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059). PMLR."},{"key":"e_1_3_2_1_12_1","volume-title":"International Conference on Machine Learning (pp. 1183-1192)","author":"Gal Y.","year":"2017","unstructured":"Gal, Y., Islam, R., & Ghahramani, Z. (2017, July). Deep bayesian active learning with image data. In International Conference on Machine Learning (pp. 1183-1192). PMLR."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2010.5509293"},{"key":"e_1_3_2_1_14_1","volume-title":"Fair active learning.\u00a0Expert Systems with Applications ,\u00a0199 , 116981","author":"Anahideh H.","year":"2022","unstructured":"Anahideh, H., Asudeh, A., & Thirumuruganathan, S. (2022). Fair active learning.\u00a0Expert Systems with Applications ,\u00a0199 , 116981."},{"key":"e_1_3_2_1_15_1","volume-title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach. In\u00a0International Conference on Learning Representations.","author":"Sener O.","year":"2018","unstructured":"Sener, O., & Savarese, S. (2018, February). Active Learning for Convolutional Neural Networks: A Core-Set Approach. In\u00a0International Conference on Learning Representations."},{"key":"e_1_3_2_1_16_1","volume-title":"Mcdal: Maximum classifier discrepancy for active learning.\u00a0IEEE transactions on neural networks and learning systems","author":"Cho J. W.","year":"2022","unstructured":"Cho, J. W., Kim, D. J., Jung, Y., & Kweon, I. S. (2022). Mcdal: Maximum classifier discrepancy for active learning.\u00a0IEEE transactions on neural networks and learning systems."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00607"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-02121-4"},{"key":"e_1_3_2_1_19_1","volume-title":"Uncertain Gradient Lower Bounds.","author":"Ash J. T.","year":"2020","unstructured":"Ash, J. T., Zhang, C., Krishnamurthy, A., Langford, J., & Agarwal, A. (2020). Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds."},{"key":"e_1_3_2_1_20_1","volume-title":"Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in neural information processing systems, 32","author":"Kirsch A.","year":"2019","unstructured":"Kirsch, A., Van Amersfoort, J., & Gal, Y. (2019). Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in neural information processing systems, 32."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106986"},{"key":"e_1_3_2_1_22_1","volume-title":"Batch mode active sampling based on marginal probability distribution matching. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(3), 1-25","author":"Chattopadhyay R.","year":"2013","unstructured":"Chattopadhyay, R., Wang, Z., Fan, W., Davidson, I., Panchanathan, S., & Ye, J. (2013). Batch mode active sampling based on marginal probability distribution matching. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(3), 1-25."},{"issue":"3","key":"e_1_3_2_1_23_1","first-page":"291","article-title":"Active Learning Optimization Method Based On Sample Redundancy","volume":"38","author":"Chunlong F.","year":"2021","unstructured":"Chunlong, F., Yixin W., Tong S., & Zhenxin Z. (2021). Active Learning Optimization Method Based On Sample Redundancy. Computer Applications and Software, 38(3), 291-297","journal-title":"Computer Applications and Software"}],"event":{"name":"ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing","acronym":"ICMLC 2023","location":"Zhuhai China"},"container-title":["Proceedings of the 2023 15th International Conference on Machine Learning and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3587716.3587803","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3587716.3587803","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:00Z","timestamp":1750183680000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3587716.3587803"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":23,"alternative-id":["10.1145\/3587716.3587803","10.1145\/3587716"],"URL":"https:\/\/doi.org\/10.1145\/3587716.3587803","relation":{},"subject":[],"published":{"date-parts":[[2023,2,17]]},"assertion":[{"value":"2023-09-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}