Search Results for author: Ken Chen

Found 8 papers, 0 papers with code

Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks

no code implementations6 Apr 2021 Yuhang Gai, Jiuming Guo, Dan Wu, Ken Chen

Reinforcement learning (RL) is always the preferred embodiment to construct the control strategy of complex tasks, like asymmetric assembly tasks.

A User-experience Driven SSIM-Aware Adaptation Approach for DASH Video Streaming

no code implementations10 Dec 2020 Mustafa Othman, Ken Chen, Anissa Mokraoui

This mechanism impacts greatly on the overall Quality of Experience (QoE) of the video streaming.

SSIM Multimedia

Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework

no code implementations27 May 2019 Zhenmao Li, Yichao Wu, Ken Chen, Yudong Wu, Shunfeng Zhou, Jiaheng Liu, Junjie Yan

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters.

Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction

no code implementations5 Dec 2018 Ken Chen, Fei Chen, Baisheng Lai, Zhongming Jin, Yong liu, Kai Li, Long Wei, Pengfei Wang, Yandong Tang, Jianqiang Huang, Xian-Sheng Hua

To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.

Traffic Prediction

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

no code implementations12 Sep 2018 Liheng Chen, Yanru Qu, Zhenghui Wang, Lin Qiu, Wei-Nan Zhang, Ken Chen, Shaodian Zhang, Yong Yu

TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance.

Graph Embedding Link Prediction

Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition

no code implementations NAACL 2018 Zhenghui Wang, Yanru Qu, Li-Heng Chen, Jian Shen, Wei-Nan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu

We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining.

Medical Named Entity Recognition NER +1

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