Search Results for author: Yajun Wang

Found 8 papers, 1 papers with code

An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

no code implementations18 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.

Bayesian Inference

Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks

no code implementations22 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.


Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations

1 code implementation18 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.

Recommendation Systems

DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation

no code implementations10 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.

Motion Segmentation

Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks

no code implementations24 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.

Click-Through Rate Prediction

Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms

no code implementations31 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.

Influence Diffusion Dynamics and Influence Maximization in Social Networks with Friend and Foe Relationships

no code implementations21 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

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