{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T13:39:38Z","timestamp":1792676378970,"version":"4.5.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030368012","type":"print"},{"value":"9783030368029","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-36802-9_40","type":"book-chapter","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T18:03:03Z","timestamp":1575568983000},"page":"376-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpreting Layered Neural Networks via Hierarchical Modular Representation"],"prefix":"10.1007","author":[{"given":"Chihiro","family":"Watanabe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,12,5]]},"reference":[{"key":"40_CR1","unstructured":"Alain, G., Bengio, Y.: Understanding intermediate layers using linear classifier probes. In: ICLR 2017 Workshop (2017)"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Ancona, M., Ceolini, E., \u00d6ztireli, A.C., Gross, M.: Towards better understanding of gradient-based attribution methods for deep neural networks. In: International Conference on Learning Representations (2018)","DOI":"10.1007\/978-3-030-28954-6_9"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.354"},{"key":"40_CR4","unstructured":"Craven, M., Shavlik, J.W.: Extracting tree-structured representations of trained networks. In: Advances in Neural Information Processing Systems, vol. 8, pp. 24\u201330 (1996)"},{"key":"40_CR5","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv:1702.08608 (2017)"},{"key":"40_CR6","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"issue":"12","key":"40_CR7","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1016\/S0031-3203(98)00181-2","volume":"32","author":"R Krishnan","year":"1999","unstructured":"Krishnan, R., Sivakumar, G., Bhattacharya, P.: Extracting decision trees from trained neural networks. Pattern Recogn. 32(12), 1999\u20132009 (1999)","journal-title":"Pattern Recogn."},{"key":"40_CR8","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE. 86, 2278\u20132324 (1998)","journal-title":"Proc. IEEE."},{"key":"40_CR9","unstructured":"Lipton, Z.C.: The mythos of model interpretability. In: Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (2016)"},{"key":"40_CR10","unstructured":"Lundberg, S.M., Lee, S.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30, pp. 4765\u20134774 (2017)"},{"key":"40_CR11","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 29, pp. 4898\u20134906 (2016)"},{"key":"40_CR12","unstructured":"Nagamine, T., Mesgarani, N.: Understanding the representation and computation of multilayer perceptrons: a case study in speech recognition. In: Proceedings of the 34th International Conference on Machine Learning, pp. 2564\u20132573 (2017)"},{"key":"40_CR13","unstructured":"Raghu, M., Gilmer, J., Yosinski, J., Sohl-Dickstein, J.: SVCCA: singular vector canonical correlation analysis for deep learning dynamics and interpretability. In: Advances in Neural Information Processing Systems, vol. 30, pp. 6076\u20136085 (2017)"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should I trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"40_CR15","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the 34th International Conference on Machine Learning, pp. 3145\u20133153 (2017)"},{"key":"40_CR16","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: ICLR 2014 Workshop (2014)"},{"key":"40_CR17","unstructured":"Singh, C., Murdoch, W.J., Yu, B.: Hierarchical interpretations for neural network predictions. In: International Conference on Learning Representations (2019)"},{"key":"40_CR18","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. In: ICLR 2015 Workshop (2015)"},{"key":"40_CR19","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning, pp. 3319\u20133328 (2017)"},{"key":"40_CR20","unstructured":"Thiagarajan, J.J., Kailkhura, B., Sattigeri, P., Ramamurthy, K.N.: Treeview: peeking into deep neural networks via feature-space partitioning. In: NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems (2016)"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Wagner, J., K\u00f6hler, J.M., Gindele, T., Hetzel, L., Wiedemer, J.T., Behnke, S.: Interpretable and fine-grained visual explanations for convolutional neural networks. In: Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00931"},{"issue":"301","key":"40_CR22","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","volume":"58","author":"JH Ward","year":"1963","unstructured":"Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236\u2013244 (1963)","journal-title":"J. Am. Stat. Assoc."},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"Watanabe, C., Hiramatsu, K., Kashino, K.: Modular representation of autoencoder networks. In: Proceedings of 2017 IEEE Symposium on Deep Learning, 2017 IEEE Symposium Series on Computational Intelligence (2017)","DOI":"10.1109\/SSCI.2017.8280859"},{"key":"40_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-319-67786-6_15","volume-title":"Discovery Science","author":"C Watanabe","year":"2017","unstructured":"Watanabe, C., Hiramatsu, K., Kashino, K.: Recursive extraction of modular structure from layered neural networks using variational Bayes method. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds.) DS 2017. LNCS (LNAI), vol. 10558, pp. 207\u2013222. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67786-6_15"},{"key":"40_CR25","unstructured":"Watanabe, C., Hiramatsu, K., Kashino, K.: Knowledge discovery from layered neural networks based on non-negative task decomposition. arXiv:1805.07137v2 (2018)"},{"key":"40_CR26","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neunet.2017.09.017","volume":"97","author":"C Watanabe","year":"2018","unstructured":"Watanabe, C., Hiramatsu, K., Kashino, K.: Modular representation of layered neural networks. Neural Netw. 97, 62\u201373 (2018)","journal-title":"Neural Netw."},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Watanabe, C., Hiramatsu, K., Kashino, K.: Understanding community structure in layered neural networks. arXiv:1804.04778 (2018)","DOI":"10.1016\/j.neucom.2019.08.020"},{"key":"40_CR28","unstructured":"Zahavy, T., Ben-Zrihem, N., Mannor, S.: Graying the black box: understanding DQNs. In: Proceedings of the 33rd International Conference on Machine Learning, pp. 1899\u20131908 (2016)"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-36802-9_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:54:12Z","timestamp":1710262452000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-36802-9_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030368012","9783030368029"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-36802-9_40","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"5 December 2019","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":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2019","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":"iconip2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ajiips.com.au\/iconip2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}