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Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to the adoption in many real world applications.
The bayesian case model: A generative approach for case-based reasoning and prototype classification
Kim, B., Rudin, C., and Shah, J. A. (2014) · 1960
Earlier work this paper cites.
Improving generalization performance using double backpropagation
Drucker, H., and Le Cun, Y. (1992) · 1992
Earlier work this paper cites.
TIMIT acoustic-phonetic continuous speech corpus
Garofolo, J. S., et al. (1993) · 1993
Earlier work this paper cites.
Extracting tree-structured representations of trained networks
Craven, M., and Shavlik, J. W. (1996) · 1996
Earlier work this paper cites.
Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid.
Kohavi, R. (1996) · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, S., and Schmidhuber, J. (1997) · 1997
Earlier work this paper cites.
Interim who clinical staging of hvi/aids and hiv/aids case definitions for surveillance: African region
Organization, W. H., et al. (2005) · 2005
Earlier work this paper cites.
Model compression
BuciluÇ, C., Caruana, R., and Niculescu-Mizil, A. (2006) · 2006
Earlier work this paper cites.
Efficient projections onto the l 1-ball for learning in high dimensions
Duchi, J., Shalev-Shwartz, S., Singer, Y., and Chandra, T. (2008) · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Maaten, L. v. d., and Hinton, G. (2008) · 2008
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Erhan, D., Bengio, Y., Courville, A., and Vincent, P. (2009) · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
Earlier work this paper cites.
Predicting response to antiretroviral treatment by machine learning: the euresist project
Zazzi, M., Incardona, F., Rosen-Zvi, M., Prosperi, M., Lengauer, T., Altmann, A., Sonnerborg, A., Lavee, T., Schulter, E., and Kaiser, R. (2012) · 2012
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Cho, K., Gulcehre, B. v. M. C., Bahdanau, D., Schwenk, F. B. H., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P., and Ba, J. (2014) · 2014
Earlier work this paper cites.
A data-driven approach to predict the success of bank telemarketing
Moro, S., Cortez, P., and Rita, P. (2014) · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V. (2014) · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W. (2015) · 2015
Earlier work this paper cites.
Bayesian dark knowledge
Balan, A. K., Rathod, V., Murphy, K. P., and Welling, M. (2015) · 2015
Cited alongside, same era.
Deep computational phenotyping
Che, Z., Kale, D., Li, W., Bahadori, M. T., and Liu, Y. (2015) · 2015
Cited alongside, same era.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W. (2015) · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J. (2015) · 2015
Cited alongside, same era.
Inceptionism: Going deeper into neural networks
Mordvintsev, A., Olah, C., and Tyka, M. (2015) · 2015
Cited alongside, same era.
Auditing black-box models for indirect influence
Adler, P., Falk, C., Friedler, S. A., Rybeck, G., Scheidegger, C., Smith, B., and Venkatasubramanian, S. (2016) · 2016
Deep patient: an unsupervised representation to predict the future of patients from the electronic health records
Miotto, R., Li, L., Kidd, B. A., and Dudley, J. T. (2016) · 2016
Later among the works it cites.
XNOR-Net: ImageNet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A. (2016) · 2016
Later among the works it cites.
Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
Later among the works it cites.
Grad-cam: Why did you say that?
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D. (2016) · 2016
Later among the works it cites.
Programs as black-box explanations
Singh, S., Ribeiro, M. T., and Guestrin, C. (2016) · 2016
Later among the works it cites.
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Cited alongside, same era.
Blackbox and derivative-free optimization: theory, algorithms and applications
Audet, C., and Kokkolaras, M. (2016) · 2016
Cited alongside, same era.
Layer-wise relevance propagation for deep neural network architectures
Binder, A., Bach, S., Montavon, G., Müller, K.-R., and Samek, W. (2016) · 2016
Cited alongside, same era.
Doctor AI: Predicting clinical events via recurrent neural networks
Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., and Sun, J. (2016) · 2016
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
Cited alongside, same era.
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · 2016
Cited alongside, same era.
Harnessing deep neural networks with logic rules
Hu, Z., Ma, X., Liu, Z., Hovy, E., and Xing, E. (2016) · 2016
Cited alongside, same era.
l1-regularized neural networks are improperly learnable in polynomial time
Zhang, Y., Lee, J. D., and Jordan, M. I. (2016) · 2016
Later among the works it cites.
Machine learning and prediction in medicine-beyond the peak of inflated expectations
Chen, J. H., Asch, S. M., et al. (2017) · 2017
Later among the works it cites.
UCI machine learning repository.
Dheeru, D., and Karra Taniskidou, E. (2017) · 2017
Later among the works it cites.
Distilling a neural network into a soft decision tree
Frosst, N., and Hinton, G. (2017) · 2017
Later among the works it cites.
Predicting intervention onset in the icu with switching state space models
Ghassemi, M., Wu, M., Hughes, M. C., Szolovits, P., and Doshi-Velez, F. (2017) · 2017
Later among the works it cites.
Understanding black-box predictions via influence functions
Koh, P. W., and Liang, P. (2017) · 2017
Later among the works it cites.
Automatic node selection for deep neural networks using group lasso regularization
Ochiai, T., Matsuda, S., Watanabe, H., and Katagiri, S. (2017) · 2017
Later among the works it cites.
Right for the right reasons: Training differentiable models by constraining their explanations
Ross, A. S., Hughes, M. C., and Doshi-Velez, F. (2017) · 2017
Later among the works it cites.
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) · 2017
Later among the works it cites.
How to train a compact binary neural network with high accuracy?
Tang, W., Hua, G., and Wang, L. (2017) · 2017
Later among the works it cites.
Highlights: Summarizing agent behavior to people
Amir, D., and Amir, O. (2018) · 2018
Later among the works it cites.
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2018) · 2018
Later among the works it cites.
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., and Müller, K.-R. (2018) · 2018
Later among the works it cites.