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Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable.
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Axiomatic attribution for deep networks
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Deep & cross network for ad click predictions
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Aaron Fisher, Cynthia Rudin, and Francesca Dominici · 2018
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Simple and scalable response prediction for display advertising
Olivier Chapelle, Eren Manavoglu, and Romer Rosales · 2015
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Innovated interaction screening for high-dimensional nonlinear classification
Yingying Fan, Yinfei Kong, Daoji Li, Zemin Zheng, et al · 2015
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Imagenet large scale visual recognition challenge
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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, et al · 2018
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xdeepfm: Combining explicit and implicit feature interactions for recommender systems
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun · 2018
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Consistent individualized feature attribution for tree ensembles
Scott M Lundberg, Gabriel G Erion, and Su-In Lee · 2018
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W James Murdoch, Peter J Liu, and Bin Yu · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Autoint: Automatic feature interaction learning via self-attentive neural networks
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang · 2018
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Define: deep convolutional neural networks accurately quantify intensities of transcription factor-dna binding and facilitate evaluation of functional non-coding variants
Meng Wang, Cheng Tai, Weinan E, and Liping Wei · 2018
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Autocross: Automatic feature crossing for tabular data in real-world applications
Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Hierarchical interpretations for neural network predictions
Chandan Singh, W James Murdoch, and Bin Yu · 2019
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Understanding impacts of high-order loss approximations and features in deep learning interpretation
Sahil Singla, Eric Wallace, Shi Feng, and Soheil Feizi · 2019
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