no code implementations • 10 Jul 2022 • Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, Deepesh Hada, Vidit Jain, Bhawna Paliwal, Anshul Mittal, Sonu Mehta, Ramachandran Ramjee, Sumeet Agarwal, Purushottam Kar, Manik Varma
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
no code implementations • 18 Oct 2021 • Yijun Xu, Jaber Valinejad, Mert Korkali, Lamine Mili, Yajun Wang, Xiao Chen, Zongsheng Zheng
To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system.
no code implementations • 22 Apr 2021 • Junhan Yang, Zheng Liu, Bowen Jin, Jianxun Lian, Defu Lian, Akshay Soni, Eun Yong Kang, Yajun Wang, Guangzhong Sun, Xing Xie
For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.
1 code implementation • 18 Feb 2021 • Jianxun Lian, Iyad Batal, Zheng Liu, Akshay Soni, Eun Yong Kang, Yajun Wang, Xing Xie
User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention.
no code implementations • 10 Mar 2020 • Chenjie Wang, Bin Luo, Yun Zhang, Qing Zhao, Lu Yin, Wei Wang, Xin Su, Yajun Wang, Chengyuan Li
The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects.
no code implementations • 24 Apr 2018 • Mehul Parsana, Krishna Poola, Yajun Wang, Zhiguang Wang
The CTR prediction problem is modeled as a supervised recurrent neural network, which naturally model the user history as a sequence of events.
no code implementations • 31 Jul 2014 • Wei Chen, Yajun Wang, Yang Yuan, Qinshi Wang
The objective of an online learning algorithm for CMAB is to minimize (\alpha,\beta)-approximation regret, which is the difference between the \alpha{\beta} fraction of the expected reward when always playing the optimal super arm, and the expected reward of playing super arms according to the algorithm.
no code implementations • 21 Nov 2011 • Yanhua Li, Wei Chen, Yajun Wang, Zhi-Li Zhang
Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied, because of their impacts on enabling effective online viral marketing.
Social and Information Networks Discrete Mathematics Physics and Society E.1; H.3.3