Yuhang Song : Publications
Journal papers
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[1]
Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach.
Yuhang Song†‚ Mai Xu†‚ Minglang Qiao‚ Jianyi Wang and Liangyu Huo.
IEEE Transactions on Pattern Analysis and Machine Intelligence 2018.
Details about Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach. | BibTeX data for Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach.
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[2]
Watching Videos with Certain and ConstantQuality: PID−based Quality Control Method.
Yuhang Song‚ Mai Xu and Shengxi Li.
Data Compression Conference 2017.
Details about Watching Videos with Certain and ConstantQuality: PID−based Quality Control Method. | BibTeX data for Watching Videos with Certain and ConstantQuality: PID−based Quality Control Method.
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[3]
Diversity−Driven Extensible Hierarchical Reinforcement Learning.
Yuhang Song†‚ Jianyi Wang†‚ Thomas Lukasiewicz‚ Zhenghua Xu and Mai Xu.
2018.
Details about Diversity−Driven Extensible Hierarchical Reinforcement Learning. | BibTeX data for Diversity−Driven Extensible Hierarchical Reinforcement Learning.
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[4]
Generalization Tower Network: A NovelDeep Neural Network Architecture for Multi−Task Learning.
Yuhang Song‚ Mai Xu and Songyang Zhang.
2017.
Details about Generalization Tower Network: A NovelDeep Neural Network Architecture for Multi−Task Learning. | BibTeX data for Generalization Tower Network: A NovelDeep Neural Network Architecture for Multi−Task Learning.
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[5]
Summable Reparameterizationsof Wasserstein Critics in the One−Dimensional Setting.
Christopher Grimm‚ Yuhang Song and Michael Littman.
2017.
Details about Summable Reparameterizationsof Wasserstein Critics in the One−Dimensional Setting. | BibTeX data for Summable Reparameterizationsof Wasserstein Critics in the One−Dimensional Setting.
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[6]
LearningApproximate Stochastic Transition Models.
Yuhang Song‚ Christopher Grimm‚ Xianming Wang and Michael Littman.
2017.
Details about LearningApproximate Stochastic Transition Models. | BibTeX data for LearningApproximate Stochastic Transition Models.
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[7]