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Learning feature interactions is important to the model performance of online advertising services.
Factorization machines
Steffen Rendle · 2010
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Ad click prediction: a view from the trenches
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et al · 2013
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Practical lessons from predicting clicks on ads at facebook
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, et al · 2014
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Higher-order factorization machines
Mathieu Blondel, Akinori Fujino, Naonori Ueda, and Masakazu Ishihata · 2016
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Training deep nets with sublinear memory cost, 2016
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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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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Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart · 2016
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Deep neural networks for youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 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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Field-aware factorization machines for ctr prediction
Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin · 2016
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Hierarchical question-image co-attention for visual question answering
Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh · 2016
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Product-based neural networks for user response prediction
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang · 2016
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Deep crossing: Web-scale modeling without manually crafted combinatorial features
Ying Shan, T Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and JC Mao · 2016
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Deep learning over multi-field categorical data
Weinan Zhang, Tianming Du, and Jun Wang · 2016
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Deepfm: a factorization-machine based neural network for ctr prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
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Neural factorization machines for sparse predictive analytics
Xiangnan He and Tat-Seng Chua · 2017
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Field-aware factorization machines in a real-world online advertising system
Yuchin Juan, Damien Lefortier, and Olivier Chapelle · 2017
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Failures of gradient-based deep learning
Shai Shalev-Shwartz, Ohad Shamir, and Shaked Shammah · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang · 2017
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Interpretable click-through rate prediction through hierarchical attention
Zeyu Li, Wei Cheng, Yang Chen, Haifeng Chen, and Wei Wang · 2020
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Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction
Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu · 2020
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DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
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Agencies agree; 2021 was a record year for ad spending, with more growth expected in 2022, Dec 2021
Brad Adgate · 2021
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Fairscale: A general purpose modular pytorch library for high performance and large scale training
Mandeep Baines, Shruti Bhosale, Vittorio Caggiano, Naman Goyal, Siddharth Goyal, Myle Ott, Benjamin Lefaudeux, Vitaliy Liptchinsky, Mike Rabbat, Sam Sheiffer, Anjali Sridhar, and Min Xu · 2021
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Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour, 2018
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 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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Nvidia tensor core programmability, performance amp; precision
Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, and Jeffrey S. Vetter · 2018
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Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2018
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Training with low-precision embedding tables
Jian Zhang, Jiyan Yang, and Hector Yuen · 2018
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Deep interest network for click-through rate prediction
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai · 2018
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Architecture and operation adaptive network for online recommendations
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Software-hardware co-design for fast and scalable training of deep learning recommendation models
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Software-hardware co-design for fast and scalable training of deep learning recommendation models, 2021
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, KR Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Ajit Mathews, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, and Vijay Rao · 2021
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Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning, 2021
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Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems
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xdeepint: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions
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Training deep learning recommendation model with quantized collective communications
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Metaformer is actually what you need for vision
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Deep learning for click-through rate estimation
Weinan Zhang, Jiarui Qin, Wei Guo, Ruiming Tang, and Xiuqiang He · 2021
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Aim: Automatic interaction machine for click-through rate prediction
Chenxu Zhu, Bo Chen, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu · 2021
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Longest-processing-time-first scheduling
Wikipedia · 2022
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Click-through rate prediction in online advertising: A literature review
Yanwu Yang and Panyu Zhai · 2022
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