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Explainability is a gateway between Artificial Intelligence and society as the current popular deep learning models are generally weak in explaining the reasoning process and prediction results.
“Learning interpretable models (phd thesis),”
S. Ruping, · 2006
Earlier work this paper cites.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman, · 2009
Earlier work this paper cites.
“Intelligible models for classification and regression,”
Yin Lou, Rich Caruana, and Johannes Gehrke, · 2012
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
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, · 2016
Cited alongside, same era.
“Harnessing deep neural networks with logic rules,”
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard H. Hovy, and Eric P. Xing, · 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
Later among the works it cites.
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA
Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, Eds., · 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.
“Interpretable machine learning: A guide for making black box models explainable,”
Christoph Molnar, · 2019
Later among the works it cites.
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