{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:22:52Z","timestamp":1759332172024,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031520372"},{"type":"electronic","value":"9783031520389"}],"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\/978-3-031-52038-9_9","type":"book-chapter","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T06:02:40Z","timestamp":1704780160000},"page":"134-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Using Logic Programming and\u00a0Kernel-Grouping for\u00a0Improving Interpretability of\u00a0Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1015-0777","authenticated-orcid":false,"given":"Parth","family":"Padalkar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2118-5425","authenticated-orcid":false,"given":"Huaduo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9727-0362","authenticated-orcid":false,"given":"Gopal","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"issue":"3\u20134","key":"9_CR1","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1017\/S1471068418000285","volume":"18","author":"J Arias","year":"2018","unstructured":"Arias, J., Carro, M., Salazar, E., Marple, K., Gupta, G.: Constraint answer set programming without grounding. Theory Pract. Logic Program. 18(3\u20134), 337\u2013354 (2018)","journal-title":"Theory Pract. Logic Program."},{"issue":"6","key":"9_CR2","doi-asserted-by":"publisher","first-page":"990","DOI":"10.3390\/electronics9060990","volume":"9","author":"G Bologna","year":"2020","unstructured":"Bologna, G., Fossati, S.: A two-step rule-extraction technique for a CNN. Electronics 9(6), 990 (2020)","journal-title":"Electronics"},{"key":"9_CR3","unstructured":"Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J.K.: This looks like that: deep learning for interpretable image recognition. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Denil, M., Demiraj, A., de Freitas, N.: Extraction of salient sentences from labelled documents (2014). https:\/\/doi.org\/10.48550\/ARXIV.1412.6815. https:\/\/arxiv.org\/abs\/1412.6815","DOI":"10.48550\/ARXIV.1412.6815"},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1613\/jair.5714","volume":"61","author":"R Evans","year":"2018","unstructured":"Evans, R., Grefenstette, E.: Learning explanatory rules from noisy data. J. Artif. Intell. Res. 61, 1\u201364 (2018)","journal-title":"J. Artif. Intell. Res."},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"Ferreira, J., de Sousa Ribeiro, M., Gon\u00e7alves, R., Leite, J.: Looking inside the black-box: logic-based explanations for neural networks. In: Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, pp. 432\u2013442, August 2022. https:\/\/doi.org\/10.24963\/kr.2022\/45","DOI":"10.24963\/kr.2022\/45"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Gelfond, M., Kahl, Y.: Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press, Cambridge (2014)","DOI":"10.1017\/CBO9781139342124"},{"key":"9_CR9","doi-asserted-by":"publisher","unstructured":"Kanagaraj, N., Hicks, D., Goyal, A., Mishra Tiwari, S., Singh, G.: Deep learning using computer vision in self driving cars for lane and traffic sign detection. Int. J. Syst. Assur. Eng. Manag. 12, May 2021. https:\/\/doi.org\/10.1007\/s13198-021-01127-6","DOI":"10.1007\/s13198-021-01127-6"},{"key":"9_CR10","doi-asserted-by":"publisher","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). https:\/\/doi.org\/10.48550\/ARXIV.1412.6980. https:\/\/arxiv.org\/abs\/1412.6980","DOI":"10.48550\/ARXIV.1412.6980"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"9_CR12","doi-asserted-by":"publisher","unstructured":"Ko, B., Kwak, S.: Survey of computer vision-based natural disaster warning systems. Opt. Eng. 51, 0901 (2012). https:\/\/doi.org\/10.1117\/1.OE.51.7.070901","DOI":"10.1117\/1.OE.51.7.070901"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Lage, I., et al.: Human evaluation of models built for interpretability. In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, vol. 7, pp. 59\u201367 (2019)","DOI":"10.1609\/hcomp.v7i1.5280"},{"key":"9_CR14","unstructured":"Law, M., Russo, A., Broda, K.: The ILASP system for inductive learning of answer set programs. arXiv:2005.00904 (2020)"},{"issue":"4","key":"9_CR15","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989). https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput."},{"issue":"11","key":"9_CR16","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(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"4","key":"9_CR17","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/BF03037089","volume":"8","author":"S Muggleton","year":"1991","unstructured":"Muggleton, S.: Inductive logic programming. New Gen. Comput. 8(4), 295\u2013318 (1991)","journal-title":"New Gen. Comput."},{"key":"9_CR18","unstructured":"Padalkar, P., Wang, H., Gupta, G.: NeSyFOLD: neurosymbolic framework for interpretable image classification (2023). https:\/\/arxiv.org\/abs\/2301.12667"},{"key":"9_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103435","volume":"292","author":"Z Qi","year":"2021","unstructured":"Qi, Z., Khorram, S., Fuxin, L.: Embedding deep networks into visual explanations. Artif. Intell. 292, 103435 (2021). https:\/\/doi.org\/10.1016\/j.artint.2020.103435","journal-title":"Artif. Intell."},{"key":"9_CR20","doi-asserted-by":"publisher","unstructured":"Ray, O.: Nonmonotonic abductive inductive learning. J. Appl. Logic 7(3), 329\u2013340 (2009). https:\/\/doi.org\/10.1016\/j.jal.2008.10.007. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1570868308000682. Special Issue: Abduction and Induction in Artificial Intelligence","DOI":"10.1016\/j.jal.2008.10.007"},{"key":"9_CR21","doi-asserted-by":"publisher","unstructured":"Reiter, R.: A logic for default reasoning. Artif. Intell. 13(1), 81\u2013132 (1980). https:\/\/doi.org\/10.1016\/0004-3702(80)90014-4. https:\/\/www.sciencedirect.com\/science\/article\/pii\/0004370280900144. Special Issue on Non-Monotonic Logic","DOI":"10.1016\/0004-3702(80)90014-4"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Sen, P., de Carvalho, B.W., Riegel, R., Gray, A.: Neuro-symbolic inductive logic programming with logical neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8212\u20138219 (2022)","DOI":"10.1609\/aaai.v36i8.20795"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Shindo, H., Nishino, M., Yamamoto, A.: Differentiable inductive logic programming for structured examples. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 5034\u20135041 (2021)","DOI":"10.1609\/aaai.v35i6.16637"},{"key":"9_CR25","doi-asserted-by":"publisher","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps (2013). https:\/\/doi.org\/10.48550\/ARXIV.1312.6034. https:\/\/arxiv.org\/abs\/1312.6034","DOI":"10.48550\/ARXIV.1312.6034"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw. 32, 323\u2013332 (2012). https:\/\/doi.org\/10.1016\/j.neunet.2012.02.016. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608012000457. Selected Papers from IJCNN 2011","DOI":"10.1016\/j.neunet.2012.02.016"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Sun, W., Zheng, B., Qian, W.: Computer aided lung cancer diagnosis with deep learning algorithms. In: SPIE Medical Imaging (2016)","DOI":"10.1117\/12.2216307"},{"key":"9_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-69535-4_13","volume-title":"Computer Vision - ACCV 2020","author":"J Townsend","year":"2021","unstructured":"Townsend, J., Kasioumis, T., Inakoshi, H.: ERIC: extracting relations inferred from convolutions. In: Ishikawa, H., Liu, C.L., Pajdla, T., Shi, J. (eds.) ACCV 2020. LNIP, vol. 12624, pp. 206\u2013222. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-69535-4_13"},{"key":"9_CR29","unstructured":"Townsend, J., Kudla, M., Raszkowska, A., Kasiousmis, T.: On the explainability of convolutional layers for multi-class problems. In: Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations (2022). https:\/\/openreview.net\/forum?id=jgVpiERy8Q8"},{"key":"9_CR30","doi-asserted-by":"publisher","unstructured":"Wang, H., Gupta, G.: FOLD-SE: an efficient rule-based machine learning algorithm with scalable explainability (2022). https:\/\/doi.org\/10.48550\/ARXIV.2208.07912","DOI":"10.48550\/ARXIV.2208.07912"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, X., Cao, Y., Wang, W., Shen, C., Huang, T.: SegGPT: towards segmenting everything in context. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1130\u20131140 (2023)","DOI":"10.1109\/ICCV51070.2023.00110"},{"key":"9_CR32","doi-asserted-by":"publisher","unstructured":"Xie, N., Sarker, M.K., Doran, D., Hitzler, P., Raymer, M.: Relating input concepts to convolutional neural network decisions (2017). https:\/\/doi.org\/10.48550\/ARXIV.1711.08006","DOI":"10.48550\/ARXIV.1711.08006"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Yang, Y., Kim, S., Joo, J.: Explaining deep convolutional neural networks via latent visual-semantic filter attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8333\u20138343 (2022)","DOI":"10.1109\/CVPR52688.2022.00815"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Yang, Z., Ishay, A., Lee, J.: NeurASP: embracing neural networks into answer set programming. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020 (2021)","DOI":"10.24963\/ijcai.2020\/243"},{"key":"9_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","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"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Cao, R., Shi, F., Wu, Y.N., Zhu, S.C.: Interpreting CNN knowledge via an explanatory graph. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11819"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Cao, R., Wu, Y.N., Zhu, S.C.: Growing interpretable part graphs on convnets via multi-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10924"},{"key":"9_CR38","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"issue":"6","key":"9_CR39","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452\u20131464 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR40","doi-asserted-by":"publisher","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5122\u20135130 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.544","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["Lecture Notes in Computer Science","Practical Aspects of Declarative Languages"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-52038-9_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T18:33:48Z","timestamp":1731004428000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-52038-9_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031520372","9783031520389"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-52038-9_9","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 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PADL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Practical Aspects of Declarative Languages","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 January 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 January 2024","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":"padl2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/popl24.sigplan.org\/home\/PADL-2024","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":"HotCRP","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","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":"13","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":"52% - 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":"4","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":"4","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)"}}]}}