1,108 research outputs found
Ensemble parameter estimation for graphical models
Parameter Estimation is one of the key issues involved in the discovery of graphical models from data. Current state of the art methods have demonstrated their abilities in different kind of graphical models. In this paper, we introduce ensemble learning into the process of parameter estimation, and examine ensemble parameter estimation methods for different kind of graphical models under complete data set and incomplete data set. We provide experimental results which show that ensemble method can achieve an improved result over the base parameter estimation method in terms of accuracy. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.<br /
An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving
Autonomous driving is the key technology of intelligent logistics in
Industrial Internet of Things (IIoT). In autonomous driving, the appearance of
incomplete point clouds losing geometric and semantic information is inevitable
owing to limitations of occlusion, sensor resolution, and viewing angle when
the Light Detection And Ranging (LiDAR) is applied. The emergence of incomplete
point clouds, especially incomplete vehicle point clouds, would lead to the
reduction of the accuracy of autonomous driving vehicles in object detection,
traffic alert, and collision avoidance. Existing point cloud completion
networks, such as Point Fractal Network (PF-Net), focus on the accuracy of
point cloud completion, without considering the efficiency of inference
process, which makes it difficult for them to be deployed for vehicle point
cloud repair in autonomous driving. To address the above problem, in this
paper, we propose an efficient deep learning approach to repair incomplete
vehicle point cloud accurately and efficiently in autonomous driving. In the
proposed method, an efficient downsampling algorithm combining incremental
sampling and one-time sampling is presented to improves the inference speed of
the PF-Net based on Generative Adversarial Network (GAN). To evaluate the
performance of the proposed method, a real dataset is used, and an autonomous
driving scene is created, where three incomplete vehicle point clouds with 5
different sizes are set for three autonomous driving situations. The improved
PF-Net can achieve the speedups of over 19x with almost the same accuracy when
compared to the original PF-Net. Experimental results demonstrate that the
improved PF-Net can be applied to efficiently complete vehicle point clouds in
autonomous driving.Comment: 10 figures, 4 table
Discovering linear causal model from incomplete data
One common drawback in algorithms for learning Linear Causal Models is that they can not deal with incomplete data set. This is unfortunate since many real problems involve missing data or even hidden variable. In this paper, based on multiple imputation, we propose a three-step process to learn linear causal models from incomplete data set. Experimental results indicate that this algorithm is better than the single imputation method (EM algorithm) and the simple list deletion method, and for lower missing rate, this algorithm can even find models better than the results from the greedy learning algorithm MLGS working in a complete data set. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.<br /
Back-action Induced Non-equilibrium Effect in Electron Charge Counting Statistics
We report our study of the real-time charge counting statistics measured by a
quantum point contact (QPC) coupled to a single quantum dot (QD) under
different back-action strength. By tuning the QD-QPC coupling or QPC bias, we
controlled the QPC back-action which drives the QD electrons out of thermal
equilibrium. The random telegraph signal (RTS) statistics showed strong and
tunable non-thermal-equilibrium saturation effect, which can be quantitatively
characterized as a back-action induced tunneling out rate. We found that the
QD-QPC coupling and QPC bias voltage played different roles on the back-action
strength and cut-off energy.Comment: 4 pages, 4 figures, 1 tabl
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