Author
Liu Songming
Bio: Liu Songming is an academic researcher from Xiamen University. The author has contributed to research in topics: Image segmentation & Deep belief network. The author has an hindex of 1, co-authored 1 publications receiving 62 citations.
Topics: Image segmentation, Deep belief network
Papers
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TL;DR: A novel image segmentation method is developed in this paper for quantitative analysis of GICS based on the deep reinforcement learning (DRL), which can accurately distinguish the test line and the control line in the GICS images.
119 citations
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TL;DR: An overview of the state-of-the-art attention models proposed in recent years is given and a unified model that is suitable for most attention structures is defined.
620 citations
TL;DR: In this article, a detailed analysis of the influence of non-IID data on both parametric and non-parametric machine learning models in both horizontal and vertical federated learning is provided.
117 citations
TL;DR: Recently, a large body of deep learning methods have been proposed and has shown great promise in handling the traditional ill-posed problem of depth estimation as discussed by the authors, which is of great significance for many applications such as augmented reality, target tracking and autonomous driving.
94 citations
TL;DR: In this article, a comprehensive review on deep multi-view learning from the following two perspectives: MVL methods in deep learning scope and deep MVL extensions of traditional methods is presented, and the authors attempt to identify some open challenges to inform future research directions.
84 citations
TL;DR: Zhang et al. as discussed by the authors proposed a smooth $L 1 -norm-oriented latent factor (SL-LF) model, which is more robust to outlier data.
Abstract: High-dimensional and sparse (HiDS) matrices commonly arise in various industrial applications, e.g., recommender systems (RSs), social networks, and wireless sensor networks. Since they contain rich information, how to accurately represent them is of great significance. A latent factor (LF) model is one of the most popular and successful ways to address this issue. Current LF models mostly adopt $L_{2}$ -norm-oriented Loss to represent an HiDS matrix, i.e., they sum the errors between observed data and predicted ones with $L_{2}$ -norm. Yet $L_{2}$ -norm is sensitive to outlier data. Unfortunately, outlier data usually exist in such matrices. For example, an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users. To address this issue, this work proposes a smooth $L_{1}$ -norm-oriented latent factor (SL-LF) model. Its main idea is to adopt smooth $L_{1}$ -norm rather than $L_{2}$ -norm to form its Loss, making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix. Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.
84 citations