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Despite outstanding contribution to the significant progress of Artificial Intelligence (AI), deep learning models remain mostly black boxes, which are extremely weak in explainability of the reasoning process and prediction results.
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S. Ruping, · 2006
Earlier work this paper cites.
“The elements of statistical learning: Data mining, inference and prediction,”
Trevor Hastie Jerome Friedman and Robert Tibshirani, · 2009
Earlier work this paper cites.
“Fast r-cnn,”
R. Girshick, · 2015
Earlier work this paper cites.
“Understanding deep features with computer-generated imagery,”
Mathieu Aubry and Bryan C. Russell, · 2015
Earlier work this paper cites.
“Going deeper with convolutions,”
C. Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, · 2015
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“Harnessing deep neural networks with logic rules,”
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard H. Hovy, and Eric P. Xing, · 2016
Earlier work this paper cites.
“Unsupervised learning on neural network outputs: With application in zero-shot learning,”
Yao Lu, · 2016
Cited alongside, same era.
“Why should I trust you?: Explaining the predictions of any classifier,”
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin, · 2016
Cited alongside, same era.
“Model-agnostic interpretability of machine learning,”
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin, · 2016
Cited alongside, same era.
“Faster r-cnn: Towards real-time object detection with region proposal networks,”
S. Ren, K. He, R. Girshick, and J. Sun, · 2017
Cited alongside, same era.
“Identifying unknown unknowns in the open world: Representations and policies for guided exploration,”
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Eric Horvitz, · 2017
Cited alongside, same era.
“Interpretable explanations of black boxes by meaningful perturbation,”
Ruth C. Fong and Andrea Vedaldi, · 2017
Later among the works it cites.
“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
Later among the works it cites.
“Interpretable convolutional neural networks,”
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu, · 2018
Later among the works it cites.
“Interpreting CNN knowledge via an explanatory graph,”
Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, and Song-Chun Zhu, · 2018
Later among the works it cites.
“Interpretable machine learning: A guide for making black box models explainable,”
Christoph Molnar, · 2019
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