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Interpretable models are designed to make decisions in a human-interpretable manner.
Liii. on lines and planes of closest fit to systems of points in space
Pearson, K · 1901
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Mnist handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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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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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
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Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
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SGDR: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Towards robust interpretability with self-explaining neural networks
Melis, D. A. and Jaakkola, T · 2018
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Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Xian, Y., Lampert, C. H., Schiele, B., and Akata, Z · 2018
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Interpretable basis decomposition for visual explanation
Zhou, B., Sun, Y., Bau, D., and Torralba, A · 2018
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This looks like that: deep learning for interpretable image recognition
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., and Su, J. K · 2019
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A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., and Dean, J · 2019
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Counterfactual visual explanations
Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., and Lee, S · 2019
Convolutional dynamic alignment networks for interpretable classifications
Bohle, M., Fritz, M., and Schiele, B · 2021
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Probabilistic embeddings for cross-modal retrieval
Chun, S., Oh, S. J., De Rezende, R. S., Kalantidis, Y., and Larlus, D · 2021
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Adamp: Slowing down the slowdown for momentum optimizers on scale-invariant weights
Heo, B., Chun, S., Oh, S. J., Han, D., Yun, S., Kim, G., Uh, Y., and Ha, J.-W · 2021
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Promises and pitfalls of black-box concept learning models
Mahinpei, A., Clark, J., Lage, I., Doshi-Velez, F., and WeiWei, P · 2021
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Meaningfully debugging model mistakes using conceptual counterfactual explanations
Abid, A., Yuksekgonul, M., and Zou, J · 2022
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Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Miller, T · 2019
Cited alongside, same era.
Modeling uncertainty with hedged instance embeddings
Oh, S. J., Gallagher, A. C., Murphy, K. P., Schroff, F., Pan, J., and Roth, J · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
Cited alongside, same era.
Probabilistic face embeddings
Shi, Y. and Jain, A. K · 2019
Cited alongside, same era.
Concept whitening for interpretable image recognition
Chen, Z., Bei, Y., and Rudin, C · 2020
Cited alongside, same era.
icaps: An interpretable classifier via disentangled capsule networks
Jung, D., Lee, J., Yi, J., and Yoon, S · 2020
Cited alongside, same era.
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: Interpretable, leak-proof concept-based models
Marconato, E., Passerini, A., and Teso, S · 2022
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A framework for learning ante-hoc explainable models via concepts
Sarkar, A., Vijaykeerthy, D., Sarkar, A., and Balasubramanian, V. N · 2022
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Learning from uncertain concepts via test time interventions
Sheth, I., Rahman, A. A., Sevyeri, L. R., Havaei, M., and Kahou, S. E · 2022
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A closer look at the intervention procedure of concept bottleneck models
Shin, S., Jo, Y., Ahn, S., and Lee, N · 2022
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Concept embedding models
Zarlenga, M. E., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Lio, P., and Jamnik, M · 2022
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Post-hoc concept bottleneck models
Yuksekgonul, M., Wang, M., and Zou, J · 2023
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