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Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target response of a given input based on its high-level concepts.
Heterogeneous uncertainty sampling for supervised learning
Lewis, D. D. and Catlett, J · 1994
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A sequential algorithm for training text classifiers: Corrigendum and additional data
Lewis, D. D · 1995
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The osteoarthritis initiative
Nevitt, M., Felson, D., and Lester, G · 2006
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Optimistic active-learning using mutual information
Guo, Y. and Greiner, R · 2007
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Multiple-instance active learning
Settles, B., Craven, M., and Ray, S · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Machine learning: Trends, perspectives, and prospects
Jordan, M. I. and Mitchell, T. M · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Earlier work this paper cites.
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Cited alongside, same era.
Towards robust interpretability with self-explaining neural networks
Alvarez Melis, D. and Jaakkola, T · 2018
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al · 2018
Cited alongside, same era.
Towards automatic concept-based explanations
Ghorbani, A., Wexler, J., Zou, J. Y., and Kim, B · 2019
Cited alongside, same era.
Concept bottleneck models
Promises and pitfalls of black-box concept learning models
Mahinpei, A., Clark, J., Lage, I., Doshi-Velez, F., and Pan, W · 2021
Later among the works it cites.
Do concept bottleneck models learn as intended?
Margeloiu, A., Ashman, M., Bhatt, U., Chen, Y., Jamnik, M., and Weller, A · 2021
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Interactive concept bottleneck models
Chauhan, K., Tiwari, R., Freyberg, J., Shenoy, P., and Dvijotham, K · 2022
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Addressing leakage in concept bottleneck models
Havasi, M., Parbhoo, S., and Doshi-Velez, F · 2022
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Glancenets: Interpretabile, leak-proof concept-based models
Marconato, E., Passerini, A., and Teso, S · 2022
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Concept bottleneck model with additional unsupervised concepts
Sawada, Y. and Nakamura, K · 2022
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Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., and Liang, P · 2020
Cited alongside, same era.
Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2020
Cited alongside, same era.
Debiasing concept-based explanations with causal analysis
Bahadori, M. T. and Heckerman, D. E · 2021
Cited alongside, same era.
Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset
Groh, M., Harris, C., Soenksen, L., Lau, F., Han, R., Kim, A., Koochek, A., and Badri, O · 2021
Cited alongside, same era.
Disparities in dermatology ai performance on a diverse, curated clinical image set
Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., et al
Cited in the paper.
Skincon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis
Daneshjou, R., Yuksekgonul, M., Cai, Z. R., Novoa, R. A., and Zou, J
Cited in the paper.
Learning from uncertain concepts via test time interventions
Sheth, I., Rahman, A. A., Sevyeri, L. R., Havaei, M., and Kahou, S. E · 2022
Later among the works it cites.
Concept embedding models
Zarlenga, M. E., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., et al · 2022
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Post-hoc concept bottleneck models
Yuksekgonul, M., Wang, M., and Zou, J · 2023
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