Search Results - "Liu, Qingshan"

A Second-Order Multi-Agent Network for Bound-Constrained Distributed Optimization by Liu, Qingshan, Wang, Jun

“…This technical note presents a second-order multi-agent network for distributed optimization with a sum of convex objective functions subject to bound…”
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A Multi-Agent System With a Proportional-Integral Protocol for Distributed Constrained Optimization by Yang, Shaofu, Liu, Qingshan, Wang, Jun

“…This technical note presents a continuous-time multi-agent system for distributed optimization with an additive objective function composed of individual…”
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Hyperspectral image classification using spectral-spatial LSTMs by Zhou, Feng, Hang, Renlong, Liu, Qingshan, Yuan, Xiaotong

“…In this paper, we propose a hyperspectral image (HSI) classification method using spectral-spatial long short term memory (LSTM) networks. Specifically, for…”
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A One-Layer Projection Neural Network for Nonsmooth Optimization Subject to Linear Equalities and Bound Constraints by Liu, Qingshan, Wang, Jun

“…This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject…”
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A Collective Neurodynamic Approach to Distributed Constrained Optimization by Liu, Qingshan, Yang, Shaofu, Wang, Jun

“…This paper presents a collective neurodynamic approach with multiple interconnected recurrent neural networks (RNNs) for distributed constrained optimization…”
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Learning Multiscale Deep Features for High-Resolution Satellite Image Scene Classification by Liu, Qingshan, Hang, Renlong, Song, Huihui, Li, Zhi

“…In this paper, we propose a multiscale deep feature learning method for high-resolution satellite image scene classification. Specifically, we first warp the…”
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Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification by Liu, Qingshan, Zhou, Feng, Hang, Renlong, Yuan, Xiaotong

“…This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the…”
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Constrained Consensus Algorithms With Fixed Step Size for Distributed Convex Optimization Over Multiagent Networks by Liu, Qingshan, Yang, Shaofu, Hong, Yiguang

“…In this technical note, we are concerned with constrained consensus algorithms for distributed convex optimization with a sum of convex objective functions…”
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Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images by Jun Xu, Lei Xiang, Qingshan Liu, Gilmore, Hannah, Jianzhong Wu, Jinghai Tang, Madabhushi, Anant

“…Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of…”
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A Projection Neural Network for Constrained Quadratic Minimax Optimization by Liu, Qingshan, Wang, Jun

“…This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient…”
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Robust Visual Tracking via Convolutional Networks Without Training by Zhang, Kaihua, Liu, Qingshan, Wu, Yi, Yang, Ming-Hsuan

“…Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However, the…”
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Cerebrospinal Fluid Inflammatory Cytokine Aberrations in Alzheimer's Disease, Parkinson's Disease and Amyotrophic Lateral Sclerosis: A Systematic Review and Meta-Analysis by Chen, Xi, Hu, Yang, Cao, Zongze, Liu, Qingshan, Cheng, Yong

“…It has been suggested that cytokine-mediated inflammation plays a key role for the onset and/or development of neurodegenerative diseases including Alzheimer's…”
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Robust Subspace Clustering With Compressed Data by Liu, Guangcan, Zhang, Zhao, Liu, Qingshan, Xiong, Hongkai

“…Dimension reduction is widely regarded as an effective way for decreasing the computation, storage, and communication loads of data-driven intelligent systems,…”
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Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit by Liu, Guangcan, Liu, Qingshan, Li, Ping

“…This paper studies the problem of recovering the authentic samples that lie on a union of multiple subspaces from their corrupted observations. Due to the…”
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Distributed Optimization Based on a Multiagent System in the Presence of Communication Delays by Yang, Shaofu, Liu, Qingshan, Wang, Jun

“…In this paper, distributed optimization is addressed based on a continuous-time multiagent system in the presence of time-varying communication delays. First,…”
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Learning Discriminative Dictionary for Group Sparse Representation by Sun, Yubao, Liu, Qingshan, Tang, Jinhui, Tao, Dacheng

“…In recent years, sparse representation has been widely used in object recognition applications. How to learn the dictionary is a key issue to sparse…”
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Joint Active Learning with Feature Selection via CUR Matrix Decomposition by Li, Changsheng, Wang, Xiangfeng, Dong, Weishan, Yan, Junchi, Liu, Qingshan, Zha, Hongyuan

“…This paper presents an unsupervised learning approach for simultaneous sample and feature selection, which is in contrast to existing works which mainly tackle…”
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A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization by Liu, Qingshan, Guo, Zhishan, Wang, Jun

“…In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound…”
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Finite-Time Convergent Recurrent Neural Network With a Hard-Limiting Activation Function for Constrained Optimization With Piecewise-Linear Objective Functions by Liu, Qingshan, Wang, Jun

“…This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective…”
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Effect of Sr on the microstructure and biodegradable behavior of Mg–Zn–Ca-Mn alloys for implant application by Wang, Jingfeng, Ma, Yao, Guo, Shengfeng, Jiang, Weiyan, Liu, Qingshan

“…In this paper, the effect of Sr on the microstructure and bio-corrosion behavior of Mg65.2−xZn30Ca4Mn0.8Srx (x = 0, 0.3, 0.5 and 0.8) amorphous matrix…”
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