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The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-understandable motivations of their decisions.
Towards automatic concept-based explanations
Ghorbani, A., Wexler, J., Zou, J., and Kim, B. (2019) · 1902
Earlier work this paper cites.
Learning symbolic physics with graph networks
Cranmer, M. D., Xu, R., Battaglia, P., and Ho, S. (2019) · 1909
Earlier work this paper cites.
The problem of simplifying truth functions
Quine, W. V. (1952) · 1952
Earlier work this paper cites.
Minimization of boolean functions
McCluskey, E. J. (1956) · 1956
Earlier work this paper cites.
The magical number seven, plus or minus two: Some limits on our capacity for processing information
Miller, G. A. (1956) · 1956
Earlier work this paper cites.
Rational choice and the structure of the environment
Simon, H. A. (1956) · 1956
Earlier work this paper cites.
Rational decision making in business organizations
Simon, H. A. (1979) · 1979
Earlier work this paper cites.
Classification and regression trees
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984) · 1984
Earlier work this paper cites.
Linear inversion of band-limited reflection seismograms
Santosa, F. and Symes, W. W. (1986) · 1986
Earlier work this paper cites.
Generalized additive models: some applications
Hastie, T. and Tibshirani, R. (1987) · 1987
Earlier work this paper cites.
Simplifying decision trees
Quinlan, J. R. (1987) · 1987
Earlier work this paper cites.
Optimal brain damage
LeCun, Y., Denker, J. S., Solla, S. A., Howard, R. E., and Jackel, L. D. (1989) · 1989
Earlier work this paper cites.
Second order derivatives for network pruning: Optimal brain surgeon
Hassibi, B. and Stork, D. G. (1993) · 1993
Earlier work this paper cites.
Fast effective rule induction
Cohen, W. W. (1995) · 1995
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · 1996
Earlier work this paper cites.
The mnist database of handwritten digits
LeCun, Y. (1998) · 1998
Earlier work this paper cites.
Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals
Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000) · 2000
Earlier work this paper cites.
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Hahnloser, R. H., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J., and Seung, H. S. (2000) · 2000
Earlier work this paper cites.
The magical number 4 in short-term memory: A reconsideration of mental storage capacity
Cowan, N. (2001) · 2001
Earlier work this paper cites.
Rule extraction from neural networks via decision tree induction
Sato, M. and Tsukimoto, H. (2001) · 2001
Earlier work this paper cites.
The case for bayesian deep learning
Wilson, A. G. (2020) · 2001
Earlier work this paper cites.
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MacKay, D. J. and Mac Kay, D. J. (2003) · 2003
Earlier work this paper cites.
Toward interpretable machine learning: Transparent deep neural networks and beyond
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., and Müller, K.-R. (2020) · 2003
Earlier work this paper cites.
Neural additive models: Interpretable machine learning with neural nets
Agarwal, R., Frosst, N., Zhang, X., Caruana, R., and Hinton, G. E. (2020) · 2004
Earlier work this paper cites.
Toward trustworthy ai development: mechanisms for supporting verifiable claims
Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · 2004
Earlier work this paper cites.
Survey of multi-objective optimization methods for engineering
Marler, R. T. and Arora, J. S. (2004) · 2004
Earlier work this paper cites.
Opportunities and challenges in explainable artificial intelligence (xai): A survey
Das, A. and Rad, P. (2020) · 2006
Earlier work this paper cites.
Domain knowledge alleviates adversarial attacks in multi-label classifiers
Melacci, S., Ciravegna, G., Sotgiu, A., Demontis, A., Biggio, B., Gori, M., and Roli, F. (2021) · 2006
Earlier work this paper cites.
Introduction to mathematical logic
Mendelson, E. (2009) · 2009
Cited alongside, same era.
Distilling free-form natural laws from experimental data
Schmidt, M. and Lipson, H. (2009) · 2009
Cited alongside, same era.
Understanding boolean function learnability on deep neural networks
Tavares, A. R., Avelar, P., Flach, J. M., Nicolau, M., Lamb, L. C., and Vardi, M. (2020) · 2009
Cited alongside, same era.
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · 2010
Cited alongside, same era.
Understanding representations learned in deep architectures
Erhan, D., Courville, A., and Bengio, Y. (2010) · 2010
Cited alongside, same era.
Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Adadi, A. and Berrada, M. (2018) · 2018
Later among the works it cites.
Interpretable machine learning in healthcare
Ahmad, M. A., Eckert, C., and Teredesai, A. (2018) · 2018
Later among the works it cites.
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Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., and Rudin, C. (2018) · 2018
Later among the works it cites.
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Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al. (2018) · 2018
Later among the works it cites.
Working with beliefs: Ai transparency in the enterprise
Chander, A., Srinivasan, R., Chelian, S., Wang, J., and Uchino, K. (2018) · 2018
Later among the works it cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Carvalho, D. V., Pereira, E. M., and Cardoso, J. S. (2019) · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F. and Hein, M. (2020) · 2020
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Adversarial learning targeting deep neural network classification: A comprehensive review of defenses against attacks
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Explanation perspectives from the cognitive sciences—a survey
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Self-training with noisy student improves imagenet classification
Xie, Q., Luong, M.-T., Hovy, E., and Le, Q. V. (2020) · 2020
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