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See detailSparse Gaussian Process Based On Hat Basis Functions
Fang, W.; Li, H.; Huang, Hui UL et al

in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (2020, August 28)

Gaussian process is a popular non-parametric Bayesian methodology for modeling the regression problem, which is completely determined by its mean and covariance function. Nevertheless, this method still ... [more ▼]

Gaussian process is a popular non-parametric Bayesian methodology for modeling the regression problem, which is completely determined by its mean and covariance function. Nevertheless, this method still has two major disadvantages: it is difficult to handle large datasets and may not meet inequality constraints in specific problems. These two issues have been addressed by the so-called sparse Gaussian process and constrained Gaussian process in recent years. In this paper, to reduce the overall computational complexity in the exact Gaussian process, we propose a new sparse Gaussian process method to solve the unconstrained regression problem. The idea is inspired by the constrained Gaussian process method. The critical point of our method is that we introduce the hat basis function, which is mentioned in the constrained Gaussian process, and modify its definition according to the range of training or test data. It turns out that this method belongs to the spectral approximation methods. Similar to the exact Gaussian process and Gaussian process with Fully Independent Training Conditional approximation, our method obtains satisfactory approximate results on analytical functions or open-source datasets. [less ▲]

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See detailCombining Deep Gaussian Process and Rule-Based Method for Decision-Making in Self-Driving Simulation with Small Data
Fang, W.; Li, H.; Dang, S. et al

in 2019 15th International Conference on Computational Intelligence and Security (CIS) (2019, March 05)

Self-driving vehicle is a popular and promising field in artificial intelligence. Conventional architecture consists of multiple sensors, which work collaboratively to sense the units on road to yield a ... [more ▼]

Self-driving vehicle is a popular and promising field in artificial intelligence. Conventional architecture consists of multiple sensors, which work collaboratively to sense the units on road to yield a precise and safe driving strategy. Besides the precision and safety, the quickness of decision is also a major concern. In order to react quickly, the vehicle need to predict its next possible action, such as acceleration, brake and steering angle, according to its latest few actions and status. In this paper, we treat this decision-making problem as a regression problem and use deep gaussian process to predict its next action. The regression model is trained using simulation data sets and accurately captures the most significant features. Combined with rule-based method, it can be used in Torcs simulation engine to realize successful loop trip on virtual roads. The proposed method outperforms the existing reinforcement learning methods on the performance indicators of time consumption and the size of data volume. It may be useful for real road tests in the future. [less ▲]

Detailed reference viewed: 87 (3 UL)