openmlsys-zh/references/explainable.bib

@ARTICLE{2020tkde_li,
  author={Li, Xiao-Hui and Cao, Caleb Chen and Shi, Yuhan and Bai, Wei and Gao, Han and Qiu, Luyu and Wang, Cong and Gao, Yuanyuan and Zhang, Shenjia and Xue, Xun and Chen, Lei},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={A Survey of Data-driven and Knowledge-aware eXplainable AI}, 
  year={2020},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TKDE.2020.2983930}}
  
@article{erhan2009visualizing,
author = {Erhan, Dumitru and Bengio, Y. and Courville, Aaron and Vincent, Pascal},
year = {2009},
month = {01},
title = {Visualizing Higher-Layer Features of a Deep Network},
journal = {Technical Report, Univeristé de Montréal}
}

@InProceedings{kim2017interpretability, 
  title={Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors ({TCAV})},
  author={Kim, Been and Wattenberg, Martin and Gilmer, Justin and Cai, Carrie and Wexler, James and Viegas, Fernanda and sayres, Rory},
  booktitle={Proceedings of the 35th International Conference on Machine Learning},
  pages={2668--2677},
  year={2018},
  volume={80},
  publisher={PMLR}
}


@InProceedings{wang2022tbnet,
  title={Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation for Explainable Recommender System},
  author={Wang, Shendi and Li, Haoyang and Cao, Caleb Chen and Li, Xiao-Hui and Ng, Ngai Fai and Liu, Jianxin and Xue, Xun and Song, Hu and Li, Jinyu, and Gu, Guangye and Chen, Lei},
  booktitle={Proceedings of 38th IEEE International Conference on Data Engineering},
  pages={Accepted},
  year={2022},
  publisher={IEEE}
}

@article{riedl2019human,
  title={Human-centered artificial intelligence and machine learning},
  author={Riedl, Mark O.},
  journal={Human Behavior and Emerging Technologies},
  volume={1},
  number={1},
  pages={33--36},
  year={2019},
  publisher={Wiley Online Library}
}

@inproceedings{ribeiro2016should,
  title={" Why should i trust you?" Explaining the predictions of any classifier},
  author={Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  booktitle={Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining},
  pages={1135--1144},
  year={2016}
}

@article{frosst2017distilling,
  title={Distilling a neural network into a soft decision tree},
  author={Frosst, Nicholas and Hinton, Geoffrey},
  journal={arXiv preprint arXiv:1711.09784},
  year={2017}
}

@article{lundberg2017unified,
  title={A unified approach to interpreting model predictions},
  author={Lundberg, Scott M and Lee, Su-In},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{chen2019looks,
  title={This looks like that: deep learning for interpretable image recognition},
  author={Chen, Chaofan and Li, Oscar and Tao, Daniel and Barnett, Alina and Rudin, Cynthia and Su, Jonathan K},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{zhang2019interpreting,
  title={Interpreting cnns via decision trees},
  author={Zhang, Quanshi and Yang, Yu and Ma, Haotian and Wu, Ying Nian},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={6261--6270},
  year={2019}
}

@inproceedings{zhang2018interpretable,
  title={Interpretable convolutional neural networks},
  author={Zhang, Quanshi and Wu, Ying Nian and Zhu, Song-Chun},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={8827--8836},
  year={2018}
}

@inproceedings{nauta2021neural,
  title={Neural prototype trees for interpretable fine-grained image recognition},
  author={Nauta, Meike and van Bree, Ron and Seifert, Christin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14933--14943},
  year={2021}
}

@inproceedings{selvaraju2017grad,
  title={Grad-cam: Visual explanations from deep networks via gradient-based localization},
  author={Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={618--626},
  year={2017}
}

@inproceedings{zeiler2014visualizing,
  title={Visualizing and understanding convolutional networks},
  author={Zeiler, Matthew D and Fergus, Rob},
  booktitle={European conference on computer vision},
  pages={818--833},
  year={2014},
  organization={Springer}
}

@article{petsiuk2018rise,
  title={Rise: Randomized input sampling for explanation of black-box models},
  author={Petsiuk, Vitali and Das, Abir and Saenko, Kate},
  journal={arXiv preprint arXiv:1806.07421},
  year={2018}
}

@inproceedings{wang2020score,
  title={Score-CAM: Score-weighted visual explanations for convolutional neural networks},
  author={Wang, Haofan and Wang, Zifan and Du, Mengnan and Yang, Fan and Zhang, Zijian and Ding, Sirui and Mardziel, Piotr and Hu, Xia},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops},
  pages={24--25},
  year={2020}
}


@inproceedings{10.1145/2988450.2988454,
  author = {Cheng, Heng-Tze and Koc, Levent and Harmsen, Jeremiah and Shaked, Tal and Chandra, Tushar and Aradhye, Hrishi and Anderson, Glen and Corrado, Greg and Chai, Wei and Ispir, Mustafa and Anil, Rohan and Haque, Zakaria and Hong, Lichan and Jain, Vihan and Liu, Xiaobing and Shah, Hemal},
  title = {Wide & Deep Learning for Recommender Systems},
  year = {2016},
  isbn = {9781450347952},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/2988450.2988454},
  doi = {10.1145/2988450.2988454},
  abstract = {Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.},
  booktitle = {Proceedings of the 1st Workshop on Deep Learning for Recommender Systems},
  pages = {7-10},
  numpages = {4},
  keywords = {Recommender Systems, Wide & Deep Learning},
  location = {Boston, MA, USA},
  series = {DLRS 2016}
}