F
Feng Bao
Researcher at Tsinghua University
Publications - 32
Citations - 1843
Feng Bao is an academic researcher from Tsinghua University. The author has contributed to research in topics: Deep learning & Artificial neural network. The author has an hindex of 12, co-authored 29 publications receiving 970 citations. Previous affiliations of Feng Bao include University of California, Berkeley & University of California, San Francisco.
Papers
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Journal ArticleDOI
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
TL;DR: This work introduces a recurrent deep neural network for real-time financial signal representation and trading and proposes a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training.
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A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification
TL;DR: It is shown how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation and the fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.
Journal ArticleDOI
Giotto: a toolbox for integrative analysis and visualization of spatial expression data
Ruben Dries,Ruben Dries,Qian Zhu,Rui Dong,Chee-Huat Linus Eng,Huipeng Li,Kan Liu,Yuntian Fu,Tianxiao Zhao,Arpan Sarkar,Feng Bao,Rani E. George,Nico Pierson,Long Cai,Guo-Cheng Yuan,Guo-Cheng Yuan +15 more
TL;DR: Giotto as discussed by the authors is an open-source toolbox for spatial data analysis and visualization that provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions.
Posted ContentDOI
Giotto, a toolbox for integrative analysis and visualization of spatial expression data
Ruben Dries,Qian Zhu,Rui Dong,Chee-Huat Linus Eng,Huipeng Li,Kan Liu,Yuntian Fu,Tianxiao Zhao,Arpan Sarkar,Feng Bao,Rani E. George,Nico Pierson,Long Cai,Guo-Cheng Yuan +13 more
TL;DR: Giotto is presented, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization and applied to a wide range of public datasets encompassing diverse technologies and platforms, demonstrating its general applicability.
Journal ArticleDOI
Cooperative Deep Reinforcement Learning for Large-Scale Traffic Grid Signal Control
TL;DR: The proposed Coder framework, which combines multiple regional agents and a centralized global agent, could reduce on average 30% congestions in terms of the number of waiting vehicles during high density traffic flows in simulations.