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Feature attribution methods are popular in interpretable machine learning.
Benchmarking Attribution Methods with Relative Feature Importance
Yang, M.; and Kim, B. 2019 · 1907
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Fully End-to-End Deep-Learning-Based Diagnosis of Pancreatic Tumors
Si, K.; Xue, Y.; Yu, X.; Zhu, X.; Li, Q.; Gong, W.; Liang, T.; and Duan, S. 2021 · 1982
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The Shapley Value: Essays in Honor of Lloyd S. Shapley
Roth, A. E. 1988 · 1988
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Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. 1992 · 1992
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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 · 2009
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Underspecification Presents Challenges for Credibility in Modern Machine Learning
D’Amour, A.; Heller, K.; Moldovan, D.; Adlam, B.; Alipanahi, B.; Beutel, A.; Chen, C.; Deaton, J.; Eisenstein, J.; Hoffman, M. D.; et al. 2020 · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
Wah, C.; Branson, S.; Welinder, P.; Perona, P.; and Belongie, S. 2011 · 2011
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Learning Attitudes and Attributes from Multi-Aspect Reviews
McAuley, J.; Leskovec, J.; and Jurafsky, D. 2012 · 2012
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Deep inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K.; Vedaldi, A.; and Zisserman, A. 2013 · 2013
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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S.; Binder, A.; Montavon, G.; Klauschen, F.; Müller, K.; and Samek, W. 2015 · 2015
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Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D.; Cho, K. H.; and Bengio, Y. 2015 · 2015
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Rationalizing Neural Predictions
Lei, T.; Barzilay, R.; and Jaakkola, T. 2016 · 2016
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Context Encoders: Feature Learning by Inpainting
Pathak, D.; Krahenbuhl, P.; Donahue, J.; Darrell, T.; and Efros, A. A. 2016 · 2016
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“Why Should I Trust You?” Explaining the Predictions of Any Classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
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Network dissection: Quantifying interpretability of deep visual representations
Bau, D.; Zhou, B.; Khosla, A.; Oliva, A.; and Torralba, A. 2017 · 2017
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A Unified Approach to Interpreting Model Predictions
Lundberg, S. M.; and Lee, S.-I. 2017 · 2017
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Evaluating the Visualization of What a Deep Neural Network Has Learned
Samek, W.; Binder, A.; Montavon, G.; Lapuschkin, S.; and Müller, K. 2017 · 2017
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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
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SmoothGrad: Removing Noise by Adding Noise
Smilkov, D.; Thorat, N.; Kim, B.; Viégas, F.; and Wattenberg, M. 2017 · 2017
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Attention is not not Explanation
Wiegreffe, S.; and Pinter, Y. 2019 · 2019
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Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control
Yu, M.; Chang, S.; Zhang, Y.; and Jaakkola, T. 2019 · 2019
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Debugging Tests for Model Explanations
Adebayo, J.; Muelly, M.; Liccardi, I.; and Kim, B. 2020 · 2020
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Understanding Global Feature Contributions With Additive Importance Measures
Covert, I.; Lundberg, S. M.; and Lee, S.-I. 2020 · 2020
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Shortcut Learning in Deep Neural Networks
Geirhos, R.; Jacobsen, J.-H.; Michaelis, C.; Zemel, R.; Brendel, W.; Bethge, M.; and Wichmann, F. A. 2020 · 2020
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Benchmarking Deep Learning Interpretability in Time Series Predictions
Ismail, A. A.; Gunady, M.; Bravo, H. C.; and Feizi, S. 2020 · 2020
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Adebayo, J.; Gilmer, J.; Muelly, M.; Goodfellow, I.; Hardt, M.; and Kim, B. 2018 · 2018
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ImageNet-Trained CNNs Are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness
Geirhos, R.; Rubisch, P.; Michaelis, C.; Bethge, M.; Wichmann, F. A.; and Brendel, W. 2018 · 2018
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Evaluating Recurrent Neural Network Explanations
Arras, L.; Osman, A.; Müller, K.-R.; and Samek, W. 2019 · 2019
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Interpretable Neural Predictions with Differentiable Binary Variables
Bastings, J.; Aziz, W.; and Titov, I. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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A Benchmark for Interpretability Methods in Deep Neural Networks
Hooker, S.; Erhan, D.; Kindermans, P.-J.; and Kim, B. 2019 · 2019
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Attention is not Explanation
Jain, S.; and Wallace, B. C. 2019 · 2019
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Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts
Jabbour, S.; Fouhey, D.; Kazerooni, E.; Sjoding, M. W.; and Wiens, J. 2020 · 2020
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Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
Jacovi, A.; and Goldberg, Y. 2020 · 2020
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Learning to Faithfully Rationalize by Construction
Jain, S.; Wiegreffe, S.; Pinter, Y.; and Wallace, B. C. 2020 · 2020
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Convolutional Neural Network Models for Cancer Type Prediction Based on Gene Expression
Mostavi, M.; Chiu, Y.-C.; Huang, Y.; and Chen, Y. 2020 · 2020
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Learning to Deceive with Attention-Based Explanations
Pruthi, D.; Gupta, M.; Dhingra, B.; Neubig, G.; and Lipton, Z. C. 2020 · 2020
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Learning Deep Features for Discriminative Localization
Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; and Torralba, A. 2016 · 2020
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Aligning Faithful Interpretations with Their Social Attribution
Jacovi, A.; and Goldberg, Y. 2021 · 2021
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The Irrationality of Neural Rationale Models
Zheng, Y.; Booth, S.; Shah, J.; and Zhou, Y. 2021 · 2021
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