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In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments.
Anomalies: The winner’s curse
Richard H Thaler. 1988 · 1988
Earlier work this paper cites.
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Yoav Benjamini and Yosef Hochberg. 1995 · 1995
Earlier work this paper cites.
The control of the false discovery rate in multiple testing under dependency
Yoav Benjamini, Daniel Yekutieli, et al · 2001
Earlier work this paper cites.
Randomization tests
Eugene Edgington and Patrick Onghena. 2007 · 2007
Earlier work this paper cites.
Learning Word Vectors for Sentiment Analysis. In Association for Computational Linguistics: Human Language Technologies (ACL) . 142–150
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
Kernel-based conditional independence test and application in causal discovery. In Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence . AUAI Press, 804–813
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2011 · 2011
Earlier work this paper cites.
Image Inpainting : Overview and Recent Advances
Christine Guillemot and Olivier Le Meur. 2014 · 2014
Earlier work this paper cites.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. In International Conference on Learning Representations (ICLR) Workshop
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations (ICLR)
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Exact post-selection inference, with application to the lasso
Jason D Lee, Dennis L Sun, Yuekai Sun, and Jonathan E Taylor. 2016 · 2016
Earlier work this paper cites.
The Mythos of Model Interpretability. In ICML Workshop on Human Interpretability in Machine Learning (WHI)
Zachary Chase Lipton. 2016 · 2016
Earlier work this paper cites.
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In International Conference on Knowledge Discovery and Data Mining (KDD)
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Rethinking the Inception Architecture for Computer Vision. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. 2016 · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, and Klaus Macherey. 2016 · 2016
Earlier work this paper cites.
Interpretable Explanations of Black Boxes by Meaningful Perturbation. In International Conference on Computer Vision (ICCV)
Ruth Fong and Andrea Vedaldi. 2017 · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions. In Neural Information Processing Systems (NeurIPS)
Scott Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Learning Important Features Through Propagating Activation Differences. In International Conference on Machine Learning (ICML) . 3145–3153
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Neural Information Processing Systems (NeurIPS)
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Semantic Image Inpainting with Perceptual and Contextual Losses
Raymond A. Yeh, Chen Chen, Teck-Yian Lim, Mark Hasegawa-Johnson, and Minh N. Do. 2017 · 2017
Mimic and Classify: A meta-algorithm for Conditional Independence Testing
Rajat Sen, Karthikeyan Shanmugam, Himanshu Asnani, Arman Rahimzamani, and Sreeram Kannan. 2018 · 2018
Later among the works it cites.
Chest X-ray Inpainting with Deep Generative Models
Ecem Sogancioglu, Shi Hu, Davide Belli, and Bram van Ginneken. 2018 · 2018
Later among the works it cites.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018a · 2018
Later among the works it cites.
Free-Form Image Inpainting with Gated Convolution
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang. 2018a · 2018
Later among the works it cites.
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Cited alongside, same era.
Sanity Checks for Saliency Maps. In Neural Information Processing Systems (NeurIPS)
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim. 2018 · 2018
Cited alongside, same era.
Redefine Statistical Significance
Daniel J Benjamin, James O Berger, Magnus Johannesson, Brian A Nosek, E-J Wagenmakers, Richard Berk, Kenneth A Bollen, Björn Brembs, Lawrence Brown, and Colin Camerer. 2018 · 2018
Cited alongside, same era.
The conditional permutation test
Thomas B Berrett, Yi Wang, Rina Foygel Barber, and Richard J Samworth. 2018 · 2018
Cited alongside, same era.
Interactive Classification for Deep Learning Interpretation
Angel Cabrera, Fred Hohman, Jason Lin, and Duen Horng Chau. 2018 · 2018
Cited alongside, same era.
Panning for gold: ‘Model-X’ knockoffs for high dimensional controlled variable selection
Emmanuel Candes, Yingying Fan, Lucas Janson, and Jinchi Lv. 2018 · 2018
Cited alongside, same era.
Explaining Image Classifiers by Adaptive Dropout and Generative In-filling. In International Conference on Learning Representations (ICLR)
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud. 2018 · 2018
Cited alongside, same era.
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. In International Conference on Machine Learning (ICML)
Jianbo Chen, Le Song, Martin J Wainwright, and Michael I Jordan. 2018 · 2018
Cited alongside, same era.
Gino Brunner, Yang Liu, Damián Pascual, Oliver Richter, Massimiliano Ciaramita, and Roger Wattenhofer. 2019 · 2019
Closest in time.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics (NAACL)
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Closest in time.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations (ICLR)
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel. 2019 · 2019
Closest in time.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. In International Conference on Learning Representations (ICLR)
Dan Hendrycks and Thomas G. Dietterich. 2019 · 2019
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Attention is not Explanation. In North American Chapter of the Association for Computational Linguistics (NAACL)
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. In Neural Information Processing Systems (NeurIPS)
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
Closest in time.
Image Inpainting Based on Patch-GANs
Liuchun Yuan, Congcong Ruan, Haifeng Hu, and Dihu Chen. 2019 · 2019
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SpanBERT: Improving Pre-training by Representing and Predicting Spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Controlling the false discovery rate via knockoffs
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