{"type":"ScholarlyArticle","title":"A Self-explaining Neural Architecture for Generalizable Concept Learning","authors":[{"name":"Sanchit Sinha","slug":"sanchit-sinha","given":"Sanchit","family":"Sinha","affiliation":"University of Virginia Charlottesville , VA , USA"},{"name":"Guangzhi Xiong","slug":"guangzhi-xiong","given":"Guangzhi","family":"Xiong","affiliation":"University of Virginia Charlottesville , VA , USA"},{"name":"Aidong Zhang","slug":"aidong-zhang","given":"Aidong","family":"Zhang","affiliation":"University of Virginia Charlottesville , VA , USA"}],"abstract":"With the wide proliferation of Deep Neural Networks in high-stake applications, there is a growing demand for explainability behind their decision-making process. Concept learning models attempt to learn high-level 'concepts' - abstract entities that align with human understanding, and thus provide interpretability to DNN architectures. However, in this paper, we demonstrate that present SOTA concept learning approaches suffer from two major problems - lack of concept fidelity wherein the models fail to learn consistent concepts among similar classes and limited concept interoperability wherein the models fail to generalize learned concepts to new domains for the same task. Keeping these in mind, we propose a novel self-explaining architecture for concept learning across domains which - i) incorporates a new concept saliency network for representative concept selection, ii) utilizes contrastive learning to capture representative domain invariant concepts, and iii) uses a novel prototype-based concept grounding regularization to improve concept alignment across domains. We demonstrate the efficacy of our proposed approach over current SOTA concept learning approaches on four widely used real-world datasets. Empirical results show that our method improves both concept fidelity measured through concept overlap and concept interoperability measured through domain adaptation performance. An appendix of the paper with more comprehensive results can also be viewed at https://arxiv.org/abs/2405.00349.","doi":"10.24963/ijcai.2024/56","url":"https://ijcai.org/proceedings/2024/56","pdf":"https://ijcai.org/proceedings/2024/0056.pdf","markdown":"https://ijcai.org/proceedings/2024/56.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/56","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":56,"pages":{"first":503,"last":511},"keywords":["AI Ethics, Trust, Fairness: ETF: Trustworthy AI","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Machine Learning: ML: Explainable/Interpretable machine learning","AI Ethics, Trust, Fairness: ETF: Explainability and interpretability"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2021] Ravi Aggarwal, Viknesh Soundera- jah, Guy Martin, Daniel SW Ting, Alan Karthikesalingam, Dominic King, Hutan Ashrafian, and Ara Darzi. Diagnos- tic accuracy of deep learning in medical imaging: A sys- tematic review and meta-analysis. NPJ digital medicine, 4(1):1-23, 2021.","title":"Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis","authors":"Ravi Aggarwal, Viknesh Sounderajah, Guy Martin, S W Daniel, Alan Ting, Dominic Karthikesalingam, Hutan King, Ara Ashrafian, Darzi","year":"2021","venue":"NPJ digital medicine","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Alvarez-Melis and Jaakkola, 2018] David Alvarez-Melis and Tommi S Jaakkola. Towards robust interpretability with self-explaining neural networks. arXiv preprint arXiv:1806.07538, 2018.","title":"Towards robust interpretability with self-explaining neural networks","authors":"Alvarez-Melis, David Jaakkola, Tommi S Alvarez-Melis, Jaakkola","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bracke et al., 2019] Philippe Bracke, Anupam Datta, Carsten Jung, and Shayak Sen. Machine learning explain- ability in finance: an application to default risk analysis. 2019.","title":"Machine learning explainability in finance: an application to default risk analysis","authors":"Philippe Bracke, Anupam Datta, Carsten Jung, Shayak Sen","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2019] Runjin Chen, Hao Chen, Jie Ren, Ge Huang, and Quanshi Zhang. Explaining neural net- works semantically and quantitatively. In ICCV, pages 9187-9196, 2019.","title":"Explaining neural networks semantically and quantitatively","authors":"Runjin Chen, Hao Chen, Jie Ren, Ge Huang, Quanshi Zhang","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen, 2020] Ting Chen. A simple framework for con- trastive learning of visual representations. In ICML. PMLR, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Ting Chen","year":"2020","venue":"ICML. PMLR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[D'Amour et al., 2020] Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al. Underspecification presents challenges for credibility in modern machine learning. JMLR, 2020.","title":"Underspecification presents challenges for credibility in modern machine learning","authors":"Katherine Alexander D'amour, Dan Heller, Ben Moldovan, Babak Adlam, Alex Alipanahi, Christina Beutel, Jonathan Chen, Jacob Deaton, Eisenstein, Matthew D Hoffman","year":"2020","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ghorbani and Zou, 2019] Amirata Ghorbani and James Zou. Data shapley: Equitable valuation of data for ma- chine learning. In ICML, pages 2242-2251. PMLR, 2019.","title":"Data shapley: Equitable valuation of data for machine learning","authors":"Zou Ghorbani, Amirata Ghorbani, James Zou","year":"2019","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Ghorbani et al., 2019] Amirata Ghorbani, James Wexler, James Zou, and Been Kim. Towards automatic concept- based explanations. NeurIPS, 2019.","title":"Towards automatic conceptbased explanations","authors":"Ghorbani","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gidaris et al., 2018] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learn- ing by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018.","title":"Unsupervised representation learning by predicting image rotations","authors":"Spyros Gidaris, Praveer Singh, Nikos Komodakis","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Goyal et al., 2019] Yash Goyal, Amir Feder, Uri Shalit, and Been Kim. Explaining classifiers with causal concept ef- fect (cace). arXiv preprint arXiv:1907.07165, 2019.","title":"Explaining classifiers with causal concept effect","authors":"Yash Goyal, Amir Feder, Uri Shalit, Been Kim","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Huang et al., 2022] Jinbin Huang, Aditi Mishra, Bum-Chul Kwon, and Chris Bryan. Conceptexplainer: Understand- ing the mental model of deep learning algorithms via interactive concept-based explanations. arXiv preprint arXiv:2204.01888, 2022.","title":"Conceptexplainer: Understanding the mental model of deep learning algorithms via interactive concept-based explanations","authors":"Huang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hull, 1994] Jonathan J. Hull. A database for handwritten text recognition research. IEEE Transactions on pattern analysis and machine intelligence, 16(5):550-554, 1994.","title":"A database for handwritten text recognition research","authors":"Jonathan J Hull","year":"1994","venue":"IEEE Transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hutchinson and Mitchell, 2019] Ben Hutchinson and Mar- garet Mitchell. 50 years of test (un) fairness: Lessons for machine learning. In FAccT, pages 49-58, 2019.","title":"Ben Hutchinson and Margaret Mitchell. 50 years of test (un) fairness: Lessons for machine learning","authors":"Mitchell Hutchinson","year":"2019","venue":"FAccT","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jeyakumar et al., 2021] Jeya Vikranth Jeyakumar, Luke Dickens, Yu-Hsi Cheng, Joseph Noor, Luis Antonio Garcia, Diego Ramirez Echavarria, Alessandra Russo, Lance M Kaplan, and Mani Srivastava. Automatic con- cept extraction for concept bottleneck-based video classi- fication. 2021.","title":"Automatic concept extraction for concept bottleneck-based video classification","authors":"Jeya Vikranth, Jeyakumar, Luke Dickens, Yu-Hsi Cheng, Joseph Noor, Luis Antonio Garcia, Diego Ramirez Echavarria, Alessandra Russo, Lance M Kaplan, Mani Srivastava","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kim et al., 2018] Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In ICML, pages 2668-2677. PMLR, 2018.","title":"Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)","authors":"Kim","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Koh and Liang, 2017] Pang Wei Koh and Percy Liang. Un- derstanding black-box predictions via influence functions. In ICML. PMLR, 2017.","title":"Understanding black-box predictions via influence functions","authors":"Pang Wei, Koh, Percy Liang","year":"2017","venue":"ICML. PMLR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Koh et al., 2020] Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. Concept bottleneck models. In ICML, pages 5338-5348. PMLR, 2020.","title":"Concept bottleneck models","authors":"Pang Wei Koh, Thao Nguyen, Siang Yew, Stephen Tang, Emma Mussmann, Been Pierson, Percy Kim, Liang","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[LeCun et al., 1998] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning ap- plied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.","title":"Gradient-based learning applied to document recognition","authors":"Yann Lecun, Léon Bottou, Yoshua Bengio, Patrick Haffner","year":"1998","venue":"Proceedings of the IEEE","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu et al., 2021] Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan, et al. Advances in deep learning-based medical image analysis. Health Data Sci- ence, 2021, 2021.","title":"Advances in deep learning-based medical image analysis","authors":"Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan","year":"2021","venue":"Health Data Science","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Murty et al., 2020] Shikhar Murty, Pang Wei Koh, and Percy Liang. Expbert: Representation engineering with natural language explanations. In ACL, pages 2106-2113, 2020.","title":"Expbert: Representation engineering with natural language explanations","authors":"Shikhar Murty, Pang Wei Koh, Percy Liang","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Netzer et al., 2011] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. Reading digits in natural images with unsupervised feature learn- ing. 2011.","title":"Reading digits in natural images with unsupervised feature learning","authors":"Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y Ng","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Peng et al., 2017] Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko. Visda: The visual domain adaptation challenge. arXiv preprint arXiv:1710.06924, 2017.","title":"Visda: The visual domain adaptation challenge","authors":"Peng","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Peng et al., 2019] Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment match- ing for multi-source domain adaptation. In ICCV, pages 1406-1415, 2019.","title":"Moment matching for multi-source domain adaptation","authors":"Peng","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Pittino et al., 2021] Federico Pittino, Vesna Dimitrievska, and Rudolf Heer. Hierarchical concept bottleneck mod- els for explainable images segmentation, objects fine clas- sification and tracking. Objects Fine Classification and Tracking, 2021.","title":"Hierarchical concept bottleneck models for explainable images segmentation, objects fine classification and tracking","authors":"Federico Pittino, Vesna Dimitrievska, Rudolf Heer","year":"2021","venue":"Objects Fine Classification and Tracking","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Raji et al., 2020] Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. Closing the ai accountability gap: Defin- ing an end-to-end framework for internal algorithmic au- diting. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 33-44, 2020.","title":"Closing the ai accountability gap: Defining an end-to-end framework for internal algorithmic auditing","authors":"Raji","year":"2020","venue":"Proceedings of the 2020 conference on fairness, accountability, and transparency","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Ribeiro et al., 2016] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Why should i trust you?: Explain- ing the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowl- edge discovery and data mining, 2016.","title":"Why should i trust you?: Explaining the predictions of any classifier","authors":"Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Rudin, 2019] Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206-215, 2019.","title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","authors":"Cynthia Rudin","year":"2019","venue":"Nature Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Saito et al., 2020] Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko. Universal domain adapta- tion through self supervision. NeurIPS, 33:16282-16292, 2020.","title":"Universal domain adaptation through self supervision","authors":"Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Kate Saenko","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sarkar et al., 2022] Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar, and Vineeth N Balasubramanian. A framework for learning ante-hoc explainable models via concepts. In CVPR, pages 10286-10295, 2022.","title":"A framework for learning ante-hoc explainable models via concepts","authors":"Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar, Vineeth N Balasubramanian","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Sawada, 2022a] Yoshihide Sawada. C-senn: Contrastive senn. 2022.","title":"Contrastive senn","authors":"Yoshihide Sawada, Sawada, C-Senn","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Sawada, 2022b] Yoshihide Sawada. Cbm with add. unsup concepts. 2022.","title":"Cbm with add","authors":"Yoshihide Sawada, Sawada","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Sinha et al., 2021] Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon, Yangfeng Ji, and Yanjun Qi. Perturbing inputs for fragile interpretations in deep natural language processing. In Proceedings of the Fourth BlackboxNLP Workshop at EMNLP, pages 420-434, 2021.","title":"Perturbing inputs for fragile interpretations in deep natural language processing","authors":"Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon, Yangfeng Ji, Yanjun Qi","year":"2021","venue":"Proceedings of the Fourth BlackboxNLP Workshop at EMNLP","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Sinha et al., 2023] Sanchit Sinha, Mengdi Huai, Jianhui Sun, and Aidong Zhang. Understanding and enhancing robustness of concept-based models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 15127-15135, 2023.","title":"Understanding and enhancing robustness of concept-based models","authors":"Sanchit Sinha, Mengdi Huai, Jianhui Sun, Aidong Zhang","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Sundararajan et al., 2017] Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep net- works. In ICML, pages 3319-3328. PMLR, 2017.","title":"Axiomatic attribution for deep networks","authors":"Mukund Sundararajan, Ankur Taly, Qiqi Yan","year":"2017","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Szepannek and Lübke, 2021] Gero Szepannek and Karsten Lübke. Facing the challenges of developing fair risk scor- ing models. Frontiers in artificial intelligence, 4, 2021.","title":"Facing the challenges of developing fair risk scoring models","authors":"Lübke ; ] Gero Szepannek, Karsten Szepannek, Lübke","year":"2021","venue":"Frontiers in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Thota and Leontidis, 2021] Mamatha Thota and Georgios Leontidis. Contrastive domain adaptation. In CVPR, pages 2209-2218, 2021.","title":"Contrastive domain adaptation","authors":"Mamatha Thota, Georgios Leontidis","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Varoquaux and Cheplygina, 2022] Gaël Varoquaux and Veronika Cheplygina. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine, 5(1):1-8, 2022.","title":"Gaël Varoquaux and Veronika Cheplygina. Machine learning for medical imaging: methodological failures and recommendations for the future","authors":"Cheplygina Varoquaux","year":"2022","venue":"NPJ digital medicine","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Venkateswara et al., 2017] Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Pan- chanathan. Deep hashing network for unsupervised do- main adaptation. In CVPR, pages 5018-5027, 2017.","title":"Deep hashing network for unsupervised domain adaptation","authors":"Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, Sethuraman Panchanathan","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang and Liu, 2021] Feng Wang and Huaping Liu. Under- standing the behaviour of contrastive loss. In CVPR, pages 2495-2504, 2021.","title":"Understanding the behaviour of contrastive loss","authors":"Wang, Liu ; ] Feng Wang, Huaping Liu","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang, 2023] Bowen Wang. Learning bottleneck concepts in image classification. In CVPR, pages 10962-10971, 2023.","title":"Learning bottleneck concepts in image classification","authors":"Bowen Wang","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Weller, 2019] Adrian Weller. Transparency: motivations and challenges. In Explainable AI: interpreting, explain- ing and visualizing deep learning, pages 23-40. Springer, 2019.","title":"Transparency: motivations and challenges","authors":"Adrian Weller","year":"2019","venue":"Explainable AI: interpreting, explaining and visualizing deep learning","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wu et al., 2020] Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, Michael R Lyu, and Yu-Wing Tai. Towards global explanations of convolutional neural networks with concept attribution. In CVPR, pages 8652- 8661, 2020.","title":"Towards global explanations of convolutional neural networks with concept attribution","authors":"Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, Yu-Wing Michael R Lyu, Tai","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xu et al., 2019] Jiaolong Xu, Liang Xiao, and Antonio M López. Self-supervised domain adaptation for computer vision tasks. IEEE Access, 7:156694-156706, 2019.","title":"Self-supervised domain adaptation for computer vision tasks","authors":"Jiaolong Xu, Liang Xiao, Antonio M López","year":"2019","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Yeh et al., 2019] Chih-Kuan Yeh, Been Kim, Sercan O Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar. On completeness-aware concept-based ex- planations in deep neural networks. arXiv preprint arXiv:1910.07969, 2019.","title":"On completeness-aware concept-based explanations in deep neural networks","authors":"Chih-Kuan Yeh, Been Kim, Sercan O Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Yu and Lin, 2023] Yu-Chu Yu and Hsuan-Tien Lin. Semi- supervised domain adaptation with source label adapta- tion. In CVPR, pages 24100-24109, 2023.","title":"Semisupervised domain adaptation with source label adaptation","authors":"Yu-Chu Yu, Hsuan-Tien Lin","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Yuksekgonul et al., 2022] Mert Yuksekgonul, Maggie Wang, and James Zou. Post-hoc concept bottleneck models. arXiv preprint arXiv:2205.15480, 2022.","title":"Post-hoc concept bottleneck models","authors":"Mert Yuksekgonul, Maggie Wang, James Zou","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zaeem and Komeili, 2021] Mohammad Nokhbeh Zaeem and Majid Komeili. Cause and effect: Concept-based ex- planation of neural networks. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2730-2736. IEEE, 2021.","title":"Cause and effect: Concept-based explanation of neural networks","authors":"Komeili Zaeem, Mohammad Nokhbeh, Zaeem, Majid Komeili","year":"2021","venue":"IEEE International Conference on Systems, Man, and Cybernetics (SMC)","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhou et al., 2018] Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba. Interpretable basis decomposition for visual explanation. In ECCV, pages 119-134, 2018.","title":"Interpretable basis decomposition for visual explanation","authors":"Zhou","year":"2018","venue":"ECCV","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}