2 code implementations • 14 Jul 2022 • Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Feng Yan, Hai Li, Yiran Chen, Wei Wen
To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i. e., search space) to search the full architectures.
no code implementations • 1 Jan 2021 • Xinshi Chen, Yan Zhu, Haowen Xu, Muhan Zhang, Liang Xiong, Le Song
We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation.
1 code implementation • 27 Aug 2020 • Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhong, Alisson Gusatti Azzolini, Chonglin Sun, Frank Jiang, Andrey Malevich, Liang Xiong
In this paper we develop a novel recommendation model that explicitly incorporates time information.
no code implementations • 7 Mar 2020 • Qinqing Zheng, Bor-Yiing Su, Jiyan Yang, Alisson Azzolini, Qiang Wu, Ou Jin, Shri Karandikar, Hagay Lupesko, Liang Xiong, Eric Zhou
Recommendation systems are often trained with a tremendous amount of data, and distributed training is the workhorse to shorten the training time.
7 code implementations • 6 Jun 2019 • Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang
The widespread application of deep learning has changed the landscape of computation in the data center.
16 code implementations • 31 May 2019 • Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
no code implementations • 4 May 2019 • Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G. Carbonell
Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into account.
no code implementations • 1 Feb 2012 • Danica J. Sutherland, Liang Xiong, Barnabás Póczos, Jeff Schneider
Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest.
no code implementations • NeurIPS 2011 • Liang Xiong, Barnabás Póczos, Jeff G. Schneider
We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.