{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T20:31:39Z","timestamp":1774729899173,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031651113","type":"print"},{"value":"9783031651120","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-65112-0_3","type":"book-chapter","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T20:21:48Z","timestamp":1721161308000},"page":"49-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Concept-Based Analysis of\u00a0Neural Networks via\u00a0Vision-Language Models"],"prefix":"10.1007","author":[{"given":"Ravi","family":"Mangal","sequence":"first","affiliation":[]},{"given":"Nina","family":"Narodytska","sequence":"additional","affiliation":[]},{"given":"Divya","family":"Gopinath","sequence":"additional","affiliation":[]},{"given":"Boyue Caroline","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Anirban","family":"Roy","sequence":"additional","affiliation":[]},{"given":"Susmit","family":"Jha","sequence":"additional","affiliation":[]},{"given":"Corina S.","family":"P\u0103s\u0103reanu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"3_CR1","unstructured":"Bai, A., Yeh, C.K., Lin, N.Y., Ravikumar, P.K., Hsieh, C.J.: Concept gradient: concept-based interpretation without linear assumption. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=_01dDd3f78"},{"key":"3_CR2","unstructured":"Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D., Nori, A., Criminisi, A.: Measuring neural net robustness with constraints. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Beland, S., et al.: Towards assurance evaluation of autonomous systems. In: Proceedings of the 39th International Conference on Computer-Aided Design, pp.\u00a01\u20136 (2020)","DOI":"10.1145\/3400302.3415785"},{"key":"3_CR4","unstructured":"Bestuzheva, K., et al.: The scip optimization suite 8.0 (2021)"},{"key":"3_CR5","unstructured":"Bommasani, R., et al.: On the opportunities and risks of foundation models. arXiv (2021). https:\/\/crfm.stanford.edu\/assets\/report.pdf"},{"key":"3_CR6","unstructured":"Burgess, C.P., et al.: Understanding disentangling in $$\\beta $$-vae. arXiv preprint arXiv:1804.03599 (2018)"},{"key":"3_CR7","unstructured":"Crabb\u00e9, J., van der Schaar, M.: Concept activation regions: a generalized framework for concept-based explanations. In: Advances in Neural Information Processing Systems, vol. 35, pp. 2590\u20132607 (2022)"},{"key":"3_CR8","unstructured":"Cunningham, E., Cobb, A.D., Jha, S.: Principal component flows. In: International Conference on Machine Learning, pp. 4492\u20134519. PMLR (2022)"},{"key":"3_CR9","doi-asserted-by":"publisher","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 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Donadello, I., Serafini, L., d\u2019Avila Garcez, A.: Logic tensor networks for semantic image interpretation. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 1596\u20131602. IJCAI (2017)","DOI":"10.24963\/ijcai.2017\/221"},{"key":"3_CR11","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"3_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-319-77935-5_9","volume-title":"NASA Formal Methods","author":"S Dutta","year":"2018","unstructured":"Dutta, S., Jha, S., Sankaranarayanan, S., Tiwari, A.: Output range analysis for deep feedforward neural networks. In: Dutle, A., Mu\u00f1oz, C., Narkawicz, A. (eds.) NFM 2018. LNCS, vol. 10811, pp. 121\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-77935-5_9"},{"issue":"1","key":"3_CR13","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s41746-020-00376-2","volume":"4","author":"A Esteva","year":"2021","unstructured":"Esteva, A., et al.: Deep learning-enabled medical computer vision. NPJ Digit. Med. 4(1), 5 (2021)","journal-title":"NPJ Digit. Med."},{"key":"3_CR14","unstructured":"Eyuboglu, S., et al.: Domino: discovering systematic errors with cross-modal embeddings. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=FPCMqjI0jXN"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Gao, I., Ilharco, G., Lundberg, S., Ribeiro, M.T.: Adaptive testing of computer vision models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4003\u20134014 (2023)","DOI":"10.1109\/ICCV51070.2023.00370"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Gopinath, D., Converse, H., Pasareanu, C., Taly, A.: Property inference for deep neural networks. In: 2019 34th IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 797\u2013809. IEEE (2019)","DOI":"10.1109\/ASE.2019.00079"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_CR18","unstructured":"Henriksen, P., Lomuscio, A.: Efficient neural network verification via adaptive refinement and adversarial search. Ph.D. thesis, Ph.D. dissertation. Imperial College London (2019)"},{"key":"3_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-63387-9_1","volume-title":"Computer Aided Verification","author":"X Huang","year":"2017","unstructured":"Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 3\u201329. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_1"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Janai, J., G\u00fcney, F., Behl, A., Geiger, A., et\u00a0al.: Computer vision for autonomous vehicles: problems, datasets and state of the art. Found. Trends\u00ae Comput. Graph. Vision 12(1\u20133), 1\u2013308 (2020)","DOI":"10.1561\/0600000079"},{"key":"3_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-319-63387-9_5","volume-title":"Computer Aided Verification","author":"G Katz","year":"2017","unstructured":"Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 97\u2013117. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_5"},{"issue":"7976","key":"3_CR22","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1038\/s41586-023-06419-4","volume":"620","author":"E Kaufmann","year":"2023","unstructured":"Kaufmann, E., Bauersfeld, L., Loquercio, A., M\u00fcller, M., Koltun, V., Scaramuzza, D.: Champion-level drone racing using deep reinforcement learning. Nature 620(7976), 982\u2013987 (2023)","journal-title":"Nature"},{"key":"3_CR23","unstructured":"Kim, B., et al.: Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a080, pp. 2668\u20132677. PMLR, 10\u201315 July 2018. https:\/\/proceedings.mlr.press\/v80\/kim18d.html"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Moayeri, M., Pope, P., Balaji, Y., Feizi, S.: A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19087\u201319097 (2022)","DOI":"10.1109\/CVPR52688.2022.01850"},{"key":"3_CR25","unstructured":"Moayeri, M., Rezaei, K., Sanjabi, M., Feizi, S.: Text-to-concept (and back) via cross-model alignment. In: International Conference on Machine Learning, pp. 25037\u201325060. PMLR (2023)"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Nanda, N., Lee, A., Wattenberg, M.: Emergent linear representations in world models of self-supervised sequence models. In: Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pp. 16\u201330 (2023)","DOI":"10.18653\/v1\/2023.blackboxnlp-1.2"},{"key":"3_CR27","unstructured":"Park, K., Choe, Y.J., Veitch, V.: The linear representation hypothesis and the geometry of large language models. In: Causal Representation Learning Workshop at NeurIPS 2023 (2023)"},{"key":"3_CR28","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 8748\u20138763. PMLR, 18\u201324 July 2021. https:\/\/proceedings.mlr.press\/v139\/radford21a.html"},{"key":"3_CR29","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"3_CR30","unstructured":"Radford, A., Sutskever, I., Kim, J.W., Krueger, G., Agarwal, S.: Clip: connecting text and images (2021)"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Singh, G., Gehr, T., P\u00fcschel, M., Vechev, M.: An abstract domain for certifying neural networks. Proc. ACM Program. Lang. 3(POPL), 1\u201330 (2019)","DOI":"10.1145\/3290354"},{"key":"3_CR32","unstructured":"Tjeng, V., Xiao, K.Y., Tedrake, R.: Evaluating robustness of neural networks with mixed integer programming. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=HyGIdiRqtm"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Toledo, F., Shriver, D., Elbaum, S., Dwyer, M.B.: Deeper notions of correctness in image-based DNNs: lifting properties from pixel to entities. In: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 2122\u20132126 (2023)","DOI":"10.1145\/3611643.3613079"},{"key":"3_CR34","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"3_CR35","unstructured":"Wang, Z., Gui, L., Negrea, J., Veitch, V.: Concept algebra for (score-based) text-controlled generative models. In: Thirty-Seventh Conference on Neural Information Processing Systems (2023)"},{"key":"3_CR36","doi-asserted-by":"publisher","unstructured":"Yeh, C., Kim, B., Ravikumar, P.: Human-centered concept explanations for neural networks. In: Hitzler, P., Sarker, M.K. (eds.) Neuro-Symbolic Artificial Intelligence: The State of the Art, Frontiers in Artificial Intelligence and Applications, vol.\u00a0342, pp. 337\u2013352. IOS Press (2021). https:\/\/doi.org\/10.3233\/FAIA210362","DOI":"10.3233\/FAIA210362"},{"key":"3_CR37","unstructured":"Zhang, Y., HaoChen, J.Z., Huang, S.C., Wang, K.C., Zou, J., Yeung, S.: Diagnosing and rectifying vision models using language. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"3_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01237-3_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"B Zhou","year":"2018","unstructured":"Zhou, B., Sun, Y., Bau, D., Torralba, A.: Interpretable basis decomposition for visual explanation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 122\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_8"}],"container-title":["Lecture Notes in Computer Science","AI Verification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-65112-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T20:22:39Z","timestamp":1721161359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-65112-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031651113","9783031651120"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-65112-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAIV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on AI Verification","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montreal, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"22 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"saiv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aiverification.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}