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Explainable machine learning significantly improves the transparency of deep neural networks.
Asirra: A captcha that exploits interest-aligned manual image categorization
Jeremy Elson, John (JD) Douceur, Jon Howell, and Jared Saul · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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On the web’s cutting edge, anonymity in name only
Emily Steel and Julia Angwin · 2010
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” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
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Transfer learning for computer vision tutorial
Sasank Chilamkurthy · 2017
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European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 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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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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Towards interpretation of recommender systems with sorted explanation paths
Fan Yang, Ninghao Liu, Suhang Wang, and Xia Hu · 2018
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Techniques for interpretable machine learning
Mengnan Du, Ninghao Liu, and Xia Hu · 2019
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Imagenette: A smaller subset of 10 easily classified classes from imagenet, March 2019
Jeremy Howard · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, et al · 2020
Learning to estimate shapley values with vision transformers
Ian Covert, Chanwoo Kim, and Su-In Lee · 2022
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Understanding dataset difficulty with v-usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Sampling permutations for shapley value estimation
Rory Mitchell, Joshua Cooper, Eibe Frank, and Geoffrey Holmes · 2022
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Singular value fine-tuning: Few-shot segmentation requires few-parameters fine-tuning
Yanpeng Sun, Qiang Chen, Xiangyu He, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jian Cheng, Zechao Li, and Jingdong Wang · 2022
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Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
Cited alongside, same era.
A theory of usable information under computational constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, and Stefano Ermon · 2020
Cited alongside, same era.
Conditional probing: measuring usable information beyond a baseline
John Hewitt, Kawin Ethayarajh, Percy Liang, and Christopher D Manning · 2021
Cited alongside, same era.
Fastshap: Real-time shapley value estimation
Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, and Rajesh Ranganath · 2021
Cited alongside, same era.
Synthetic benchmarks for scientific research in explainable machine learning
Yang Liu, Sujay Khandagale, Colin White, and Willie Neiswanger · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Guanchu Wang, Yu-Neng Chuang, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, and Xia Hu · 2022
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Tutorial on deep learning interpretation: A data perspective
Zhou Yang, Ninghao Liu, Xia Ben Hu, and Fang Jin · 2022
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Towards fair patient-trial matching via patient-criterion level fairness constraint
Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan, Kai Zhang, Xiaoqian Jiang, Xia Hu, and Na Zou · 2023
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Harsanyinet: Computing accurate shapley values in a single forward propagation
Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, and Quanshi Zhang · 2023
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Efficient xai techniques: A taxonomic survey
Yu-Neng Chuang, Guanchu Wang, Fan Yang, Zirui Liu, Xuanting Cai, Mengnan Du, and Xia Hu · 2023
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Cortx: Contrastive framework for real-time explanation
Yu-Neng Chuang, Guanchu Wang, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, and Xia Hu · 2023
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Efficient gnn explanation via learning removal-based attribution
Yao Rong, Guanchu Wang, Qizhang Feng, Ninghao Liu, Zirui Liu, Enkelejda Kasneci, and Xia Hu · 2023
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Explain any concept: Segment anything meets concept-based explanation
Ao Sun, Pingchuan Ma, Yuanyuan Yuan, and Shuai Wang · 2023
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