Titlebook: Weak Convergence and Empirical Processes; With Applications to Aad W. Vaart,Jon A. Wellner Book 19961st edition Springer Science+Business M

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Aad W. van der Vaart,Jon A. Wellnere the gap, color normalization is a prerequisite for most CAD algorithms. The existing algorithms with better normalization effect often require more computational consumption, resisting the fast application in large-size medical stain slide images. This paper designs a fast normalization network (F

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Aad W. van der Vaart,Jon A. Wellnertion of cable-driven robots is key to attain desirable levels of cost and performance. In this paper, we investigate the optimal configuration of a cable-driven parallel mechanism under topologically distinct tasks by using gradient-free heuristics with distinct modes of exploration and exploitation

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Aad W. van der Vaart,Jon A. Wellnerch. However, its identification specifically through experimental approaches is extremely time consuming and labor-intensive task. Several machine learning methodologies have been proposed to accurately discriminate enhancers from regulatory elements and to estimate their strength. Existing approach

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Aad W. van der Vaart,Jon A. Wellnerosed every year. Despite promising results demonstrated in various sparse reward environments, this domain lacks a unified definition of a sparse reward environment and an experimentally fair way to compare existing algorithms. These issues significantly affect the in-depth analysis of the underlyin

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Aad W. van der Vaart,Jon A. Wellnertinuously for some time. It is because those sensors are exposed to high humid conditions regularly. In a sense, data read from the humidity sensor is noisier than those from other sensors deployed in the greenhouse. Therefore, this paper proposes a simple data-driven technique based on two nested K

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Aad W. van der Vaart,Jon A. Wellnerver, increasingly complex network designs cause huge computational budgets. Therefore, a more efficient structure for SISR task is desirable. In this report, we propose a novel structure, called G-HAPNet. Specifically, the group-hierarchical atrous pyramid block (G-HAPB) is built to package as a gen

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Aad W. van der Vaart,Jon A. Wellner. Deep learning-based methods have been used to assist radiologists in diagnosis, with remarkable achievements. However, obtaining sufficient labeled data is time-consuming and labor-intensive. Semi-supervised learning is an effective way to alleviate dependence on annotated data by combining unlabe

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Aad W. van der Vaart,Jon A. Wellnerence on Neural Information Processing, ICONIP 2007, held in Kitakyushu, Japan, in November 2007, jointly with BRAINIT 2007, the 4th International Conference on Brain-Inspired Information Technology...The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary pa

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Aad W. van der Vaart,Jon A. Wellnerntly relied on manually extracted features from EEG signals. It remains largely unexplored in the utilization of raw EEG signals, which contain more temporal information but present a significant challenge due to their abundance of redundant data and susceptibility to contamination from other physio

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Aad W. van der Vaart,Jon A. Wellnerhe performance of agents, and ignores the generality of the trained model on different tasks. This paper aims to enhance the generality of reinforcement learning, which makes the trained model easily adapt to different combating tasks with a variable number of agents. We divide the observation of an