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Saliency methods have been widely used to highlight important input features in model predictions.
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne · 2016
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Predicting the future—big data, machine learning, and clinical medicine
Ziad Obermeyer and Ezekiel J Emanuel · 2016
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Wavenet: A generative model for raw audio
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Michael L Rich · 2016
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Self-erasing network for integral object attention
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Tell me where to look: Guided attention inference network
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu · 2018
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The mythos of model interpretability
Zachary C Lipton · 2018
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Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal · 2018
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Nicholas Frosst and Geoffrey Hinton · 2017
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Learning how to explain neural networks: Patternnet and patternattribution
Pieter-Jan Kindermans, Kristof T Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, and Sven Dähne · 2017
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Temporal convolutional networks for action segmentation and detection
Colin Lea, Michael Flynn, Rene Vidal, Austin Reiter, and Gregory Hager · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Beyond sparsity: Tree regularization of deep models for interpretability
Mike Wu, Michael C Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, and Finale Doshi-Velez · 2018
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Eraser: A benchmark to evaluate rationalized nlp models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace · 2019
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Saliency learning: Teaching the model where to pay attention
Reza Ghaeini, Xiaoli Z Fern, Hamed Shahbazi, and Prasad Tadepalli · 2019
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Interpretation of neural networks is fragile
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Input-cell attention reduces vanishing saliency of recurrent neural networks
Aya Abdelsalam Ismail, Mohamed Gunady, Luiz Pessoa, Hector Corrada Bravo, and Soheil Feizi · 2019
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The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2019
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Certifiably robust interpretation in deep learning
Alexander Levine, Sahil Singla, and Soheil Feizi · 2019
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Understanding impacts of high-order loss approximations and features in deep learning interpretation
Sahil Singla, Eric Wallace, Shi Feng, and Soheil Feizi · 2019
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Sharpen focus: Learning with attention separability and consistency
Lezi Wang, Ziyan Wu, Srikrishna Karanam, Kuan-Chuan Peng, Rajat Vikram Singh, Bo Liu, and Dimitris N Metaxas · 2019
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Debugging tests for model explanations
Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim · 2020
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Benchmarking deep learning interpretability in time series predictions
Aya Abdelsalam Ismail, Mohamed Gunady, Héctor Corrada Bravo, and Soheil Feizi · 2020
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Sanity checks for saliency metrics
Richard Tomsett, Dan Harborne, Supriyo Chakraborty, Prudhvi Gurram, and Alun Preece · 2020
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What went wrong and when? instance-wise feature importance for time-series models
Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, and Anna Goldenberg · 2020
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265 bird species, 2021
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