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This study proves the two-phase dynamics of a deep neural network (DNN) learning interactions.
A simplified bargaining model for the n-person cooperative game
John C. Harsanyi · 1963
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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An axiomatic approach to the concept of interaction among players in cooperative games
Michel Grabisch and Marc Roubens · 1999
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The Caltech-UCSD Birds-200-2011 Dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Interpretable deep models for icu outcome prediction
Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu · 2016
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A scalable active framework for region annotation in 3d shape collections
Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas · 2016
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey Hinton · 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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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory sayres · 2018
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Considerations when learning additive explanations for black-box models
Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, and Rich Caruana · 2018
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Explainable neural networks based on additive index models
Joel Vaughan, Agus Sudjianto, Erind Brahimi, Jie Chen, and Vijayan N Nair · 2018
A Unified Approach to Interpreting and Boosting Adversarial Transferability
Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, and Quanshi Zhang · 2021
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Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability
Quanshi Zhang, Xin Wang, Jie Ren, Xu Cheng, Shuyun Lin, Yisen Wang, and Xiangming Zhu · 2022
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Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs
Mingjie Li and Quanshi Zhang · 2023
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Does a Neural Network Really Encode Symbolic Concepts?
Mingjie Li and Quanshi Zhang · 2023
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Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities
Dongrui Liu, Huiqi Deng, Xu Cheng, Qihan Ren, Kangrui Wang, and Quanshi Zhang · 2023
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon · 2019
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The shapley taylor interaction index
Mukund Sundararajan, Kedar Dhamdhere, and Ashish Agarwal · 2020
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A Game-Theoretic Taxonomy of Visual Concepts in DNNs
Xu Cheng, Chuntung Chu, Yi Zheng, Jie Ren, and Quanshi Zhang · 2021
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A Hypothesis for the Aesthetic Appreciation in Neural Networks
Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, and Quanshi Zhang · 2021
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Defining and Quantifying the Emergence of Sparse Concepts in DNNs
Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, and Quanshi Zhang · 2023
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Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
Jie Ren, Zhanpeng Zhou, Qirui Chen, and Quanshi Zhang · 2023
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Bayesian Neural Networks Tend to Ignore Complex and Sensitive Concepts
Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, and Quanshi Zhang · 2023
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Explaining how a neural network play the go game and let people learn
Huilin Zhou, Huijie Tang, Mingjie Li, Hao Zhang, Zhenyu Liu, and Quanshi Zhang · 2023
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Defining and extracting generalizable interaction primitives from DNNs
Lu Chen, Siyu Lou, Benhao Huang, and Quanshi Zhang · 2024
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Unifying Fourteen Post-hoc Attribution Methods with Taylor Interactions
Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, and Quanshi Zhang · 2024
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Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs
Qihan Ren, Jiayang Gao, Wen Shen, and Quanshi Zhang · 2024
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Junpeng Zhang, Qing Li, Liang Lin, and Quanshi Zhang · 2024
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Explaining Generalization Power of a DNN using Interactive Concepts
Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, and Quanshi Zhang · 2024
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