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The significance of coronary calcification detected by fluoroscopy: a report of 360 patients
A. G. Bartel, J. T. Chen, R. H. Peter, V. S. Behar, Y. Kong, and R. G. Lester · 1974
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Cardiac cinefluoroscopy as an inexpensive aid in the diagnosis of coronary artery disease
R. Detrano, E. E. Salcedo, R. E. Hobbs, and J. Yiannikas · 1986
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International application of a new probability algorithm for the diagnosis of coronary artery disease
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J.-J. Schmid, S. Sandhu, K. H. Guppy, S. Lee, and V. Froelicher · 1989
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An axiomatic approach to the concept of interaction among players in cooperative games
M. Grabisch and M. Roubens · 1999
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Biocarta
D. Nishimura · 2001
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The kegg database
M. Kanehisa et al · 2002
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Capturing heterogeneity in gene expression studies by surrogate variable analysis
J. T. Leek and J. D. Storey · 2007
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Effective diagnosis of heart disease through neural networks ensembles
R. Das, I. Turkoglu, and A. Sengur · 2009
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On the computational complexity of weighted voting games
E. Elkind, L. A. Goldberg, P. W. Goldberg, and M. Wooldridge · 2009
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Visualizing higher-layer features of a deep network
D. Erhan, Y. Bengio, A. Courville, and P. Vincent · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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The high time resolution universe pulsar survey–i. system configuration and initial discoveries
M. Keith, A. Jameson, W. Van Straten, M. Bailes, S. Johnston, M. Kramer, A. Possenti, S. Bates, N. Bhat, M. Burgay, et al · 2010
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An efficient explanation of individual classifications using game theory
I. Kononenko et al · 2010
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Gender differences in coronary heart disease
A. H. Maas and Y. E. Appelman · 2010
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Lstm neural networks for language modeling
M. Sundermeyer, R. Schlüter, and H. Ney · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts · 2013
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On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
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The reactome pathway knowledgebase
D. Croft, A. F. Mundo, R. Haw, M. Milacic, J. Weiser, G. Wu, M. Caudy, P. Garapati, M. Gillespie, M. R. Kamdar, et al · 2014
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Convolutional neural networks for sentence classification
Y. Kim · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Current understanding of coronary artery calcification
W. Liu, Y. Zhang, C.-M. Yu, Q.-W. Ji, M. Cai, Y.-X. Zhao, and Y.-J. Zhou · 2015
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Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
A. Binder, G. Montavon, S. Lapuschkin, K.-R. Müller, and W. Samek · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach
R. J. Lyon, B. Stappers, S. Cooper, J. Brooke, and J. Knowles · 2016
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" why should i trust you?" explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Attention-based lstm for aspect-level sentiment classification
Y. Wang, M. Huang, X. Zhu, and L. Zhao · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
Cited alongside, same era.
B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, and R. Sayres · 2017
Cited alongside, same era.
Interactive visualization and manipulation of attention-based neural machine translation
J. Lee, J.-H. Shin, and J.-S. Kim · 2017
Cited alongside, same era.
A structured self-attentive sentence embedding
Z. Lin, M. Feng, C. N. d. Santos, M. Yu, B. Xiang, B. Zhou, and Y. Bengio · 2017
Drugbank 5.0: a major update to the drugbank database for 2018
D. S. Wishart, Y. D. Feunang, A. C. Guo, E. J. Lo, A. Marcu, J. R. Grant, T. Sajed, D. Johnson, C. Li, Z. Sayeeda, et al · 2018
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Beyond sparsity: Tree regularization of deep models for interpretability
M. Wu, M. C. Hughes, S. Parbhoo, M. Zazzi, V. Roth, and F. Doshi-Velez · 2018
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On identifiability in transformers
G. Brunner, Y. Liu, D. Pascual, O. Richter, M. Ciaramita, and R. Wattenhofer · 2019
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Robust attribution regularization
J. Chen, X. Wu, V. Rastogi, Y. Liang, and S. Jha · 2019
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Recovering pairwise interactions using neural networks
T. Cui, P. Marttinen, and S. Kaski · 2019
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Cited alongside, same era.
A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
Cited alongside, same era.
Feature visualization
C. Olah, A. Mordvintsev, and L. Schubert · 2017
Cited alongside, same era.
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
K. Preuer, R. P. I. Lewis, S. Hochreiter, A. Bender, K. C. Bulusu, and G. Klambauer · 2017
Cited alongside, same era.
Magix: Model agnostic globally interpretable explanations
N. Puri, P. Gupta, P. Agarwal, S. Verma, and B. Krishnamurthy · 2017
Cited alongside, same era.
Searching for activation functions
P. Ramachandran, B. Zoph, and Q. V. Le · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
Cited alongside, same era.
K. Dhamdhere, A. Agarwal, and M. Sundararajan · 2019
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Explanations can be manipulated and geometry is to blame
A.-K. Dombrowski, M. Alber, C. Anders, M. Ackermann, K.-R. Müller, and P. Kessel · 2019
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Learning explainable models using attribution priors
G. Erion, J. D. Janizek, P. Sturmfels, S. Lundberg, and S.-I. Lee · 2019
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Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement
J. R. Geis, A. P. Brady, C. C. Wu, J. Spencer, E. Ranschaert, J. L. Jaremko, S. G. Langer, A. Borondy Kitts, J. Birch, W. F. Shields, et al · 2019
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Interpretation of neural networks is fragile
A. Ghorbani, A. Abid, and J. Zou · 2019
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A benchmark for interpretability methods in deep neural networks
S. Hooker, D. Erhan, P.-J. Kindermans, and B. Kim · 2019
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S. Jain and B. C. Wallace · 2019
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Segment integrated gradients: Better attributions through regions
A. Kapishnikov, T. Bolukbasi, F. Viégas, and M. Terry · 2019
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The (un) reliability of saliency methods
P.-J. Kindermans, S. Hooker, J. Adebayo, M. Alber, K. T. Schütt, S. Dähne, D. Erhan, and B. Kim · 2019
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V. Lai, J. Z. Cai, and C. Tan · 2019
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Z. Q. Lin, M. J. Shafiee, S. Bochkarev, M. S. Jules, X. Y. Wang, and A. Wong · 2019
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Incorporating priors with feature attribution on text classification
F. Liu and B. Avci · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
V. Sanh, L. Debut, J. Chaumond, and T. Wolf · 2019
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S. Serrano and N. A. Smith · 2019
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Understanding impacts of high-order loss approximations and features in deep learning interpretation
S. Singla, E. Wallace, S. Feng, and S. Feizi · 2019
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The many shapley values for model explanation
M. Sundararajan and A. Najmi · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, and J. Brew · 2019
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On the (in) fidelity and sensitivity of explanations
C.-K. Yeh, C.-Y. Hsieh, A. Suggala, D. I. Inouye, and P. K. Ravikumar · 2019
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From local explanations to global understanding with explainable ai for trees
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee · 2020
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
D. Slack, S. Hilgard, E. Jia, S. Singh, and H. Lakkaraju · 2020
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Visualizing the impact of feature attribution baselines
P. Sturmfels, S. Lundberg, and S.-I. Lee · 2020
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Meaningful information and the right to explanation
A. D. Selbst and J. Powles · 2044
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