no code implementations • 22 Aug 2021 • Kai-Hsun Chen, Huan-Ping Su, Wei-Chiu Chuang, Hung-Chang Hsiao, Wangda Tan, Zhankun Tang, Xun Liu, Yanbo Liang, Wen-Chih Lo, Wanqiang Ji, Byron Hsu, Keqiu Hu, HuiYang Jian, Quan Zhou, Chien-Min Wang
As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists to improve their productivity.
The success of the proposed traffic boundary control is demonstrated by simulation of traffic congestion control in Center City Philadelphia.
By classifying the streets as inlets, outlets, and interior nodes, the model predictive control (MPC) method is applied to alleviate the network traffic congestion by optimizing the traffic inflow and outflow across the boundary of the NOIR with consideration of the inner traffic dynamics as a stochastic process.
Selective attention plays an essential role in information acquisition and utilization from the environment.
With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved.
The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment.
Then we extend the model family to a variety of bayesian online models with increasing feature embedding capabilities, such as Sparse-MLP, FM-MLP and FFM-MLP.