Dong Chen Qin

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All published works

Action	Title	Year	Authors
+ PDF Chat	A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (DoRaR)	2024	Dong Chen Qin George T. Amariucai Daji Qiao Yong Guan Fu Shen

Common Coauthors

Coauthor	Papers Together
George T. Amariucai	1
Fu Shen	1
Daji Qiao	1
Yong Guan	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Understanding Neural Networks Through Deep Visualization	2015	Jason Yosinski Jeff Clune Anh Mai Nguyen Thomas J. Fuchs Hod Lipson	1
+ PDF Chat	Understanding deep image representations by inverting them	2015	Aravindh Mahendran Andrea Vedaldi	1
+ PDF Chat	Deep neural networks are easily fooled: High confidence predictions for unrecognizable images	2015	Anh‐Tu Nguyen Jason Yosinski Jeff Clune	1
+	Categorical Reparameterization with Gumbel-Softmax	2016	Eric Jang Shixiang Gu Ben Poole	1
+	Visualizing Deep Neural Network Decisions: Prediction Difference Analysis	2017	Luisa Zintgraf Taco Cohen Tameem Adel Max Welling	1
+ PDF Chat	Towards Explanation of DNN-based Prediction with Guided Feature Inversion	2018	Mengnan Du Ninghao Liu Qingquan Song Xia Hu	1
+	Fooling Neural Network Interpretations via Adversarial Model Manipulation	2019	Juyeon Heo Sunghwan Joo Taesup Moon	1
+ PDF Chat	Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach	2021	Seojin Bang Pengtao Xie Heewook Lee Wei Wu Eric P. Xing	1
+	Explanations can be manipulated and geometry is to blame	2019	Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders Marcel R. Ackermann Klaus‐Robert Müller Pan Kessel	1
+	Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps	2013	Karen Simonyan Andrea Vedaldi Andrew Zisserman	1
+ PDF Chat	Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization	2017	Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra	1
+	A Unified Approach to Interpreting Model Predictions	2017	Scott Lundberg Su‐In Lee	1
+	Real Time Image Saliency for Black Box Classifiers	2017	Piotr Dabkowski Yarin Gal	1
+	A Benchmark for Interpretability Methods in Deep Neural Networks	2018	Sara Hooker Dumitru Erhan Pieter-Jan Kindermans Been Kim	1
+ PDF Chat	Techniques for interpretable machine learning	2019	Mengnan Du Ninghao Liu Xia Hu	1
+ PDF Chat	Interpretable Explanations of Black Boxes by Meaningful Perturbation	2017	Ruth Fong Andrea Vedaldi	1
+ PDF Chat	The Mythos of Model Interpretability	2018	Zachary C. Lipton	1
+	Self-explaining Neural Network with Plausible Explanations.	2021	Sayantan Kumar Sean Yu Andrew P. Michelson Philip Payne	1
+ PDF Chat	Towards Self-Explainable Graph Neural Network	2021	Enyan Dai Suhang Wang	1
+	A Consistent and Efficient Evaluation Strategy for Attribution Methods	2022	Yao Rong Tobias Leemann Vadim Borisov Gjergji Kasneci Enkelejda Kasneci	1
+	On the Robustness of Interpretability Methods	2018	David Alvarez-Melis Tommi Jaakkola	1
+	SmoothGrad: removing noise by adding noise	2017	Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viégas Martin Wattenberg	1