Search Results for author: Yong Ren

Found 7 papers, 1 papers with code

Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay

no code implementations1 Feb 2022 Wei Wei, Jingjing Wang, Jun Du, Zhengru Fang, Chunxiao Jiang, Yong Ren

Simulations show that underwater disturbances have a large impact on the system considering communication delay.


SDN-based Resource Allocation in Edge and Cloud Computing Systems: An Evolutionary Stackelberg Differential Game Approach

no code implementations26 Sep 2021 Jun Du, Chunxiao Jiang, Abderrahim Benslimane, Song Guo, Yong Ren

Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs).


Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

no code implementations24 Jan 2019 Jingjing Wang, Chunxiao Jiang, Haijun Zhang, Yong Ren, Kwang-cheng Chen, Lajos Hanzo

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services.

Decision Making

Smooth Neighbors on Teacher Graphs for Semi-supervised Learning

1 code implementation CVPR 2018 Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, Bo Zhang

In SNTG, a graph is constructed based on the predictions of the teacher model, i. e., the implicit self-ensemble of models.

Kernel Bayesian Inference with Posterior Regularization

no code implementations NeurIPS 2016 Yang Song, Jun Zhu, Yong Ren

We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution.

Bayesian Inference

Conditional Generative Moment-Matching Networks

no code implementations NeurIPS 2016 Yong Ren, Jialian Li, Yucen Luo, Jun Zhu

Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding.

Spectral Learning for Supervised Topic Models

no code implementations19 Feb 2016 Yong Ren, Yining Wang, Jun Zhu

Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees.

Topic Models

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