Search Results for author: Xin Luo

Found 41 papers, 7 papers with code

Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model

1 code implementation19 Feb 2024 Jialiang Wang, Weiling Li, Yurong Zhong, Xin Luo

The performance of an LFA model relies heavily on its training process, which is a non-convex optimization.

Recommendation Systems

Bias Mitigating Few-Shot Class-Incremental Learning

no code implementations1 Feb 2024 Li-Jun Zhao, Zhen-Duo Chen, Zi-Chao Zhang, Xin Luo, Xin-Shun Xu

Some recent methods somewhat alleviate the accuracy imbalance between base and incremental classes by fine-tuning the feature extractor in the incremental sessions, but they further cause the accuracy imbalance between past and current incremental classes.

Few-Shot Class-Incremental Learning Incremental Learning

Federated Class-Incremental Learning with Prompting

no code implementations13 Oct 2023 Jiale Liu, Yu-Wei Zhan, Chong-Yu Zhang, Xin Luo, Zhen-Duo Chen, Yinwei Wei, Xin-Shun Xu

For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid).

Class Incremental Learning Federated Learning +1

A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis

no code implementations19 Sep 2023 Yurong Zhong, Zhe Xie, Weiling Li, Xin Luo

Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a linear bias incorporation (LBI) scheme is important in present the training overshooting and fluctuation, as well as preventing the model from premature convergence.

Computational Efficiency Representation Learning

On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement

1 code implementation ICCV 2023 Xin Luo, Yunan Zhu, Shunxin Xu, Dong Liu

We tackle this issue by examining the spectral discriminators in the context of perceptual image super-resolution (i. e., GAN-based SR), as SR image quality is susceptible to spectral changes.

Image Super-Resolution No-Reference Image Quality Assessment

An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition

no code implementations19 Jun 2023 Liping Zhang, Di wu, Xin Luo

Then, based on the idea of stacking ensemble, long short-term memory is employed as an error correction module to forecast the components separately, and the forecast results are treated as new features to be fed into extreme gradient boosting for the second-step forecasting.

Feature Engineering Load Forecasting +2

Multi-constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Large-scale Undirected Weighted Networks

no code implementations6 Jun 2023 Yurong Zhong, Zhe Xie, Weiling Li, Xin Luo

An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e. g., a protein interaction network from a bioinformatics application.

Representation Learning

Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution

1 code implementation31 May 2023 Manchang Jin, Gaosheng Liu, Kunshu Hu, Xin Luo, Kun Li, Jingyu Yang

Recent learning-based approaches have achieved significant progress in light field (LF) image super-resolution (SR) by exploring convolution-based or transformer-based network structures.

Image Super-Resolution

Online Sparse Streaming Feature Selection Using Adapted Classification

no code implementations25 Feb 2023 Ruiyang Xu, Di wu, Xin Luo

Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly.

Classification Feature Correlation +1

A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection

no code implementations23 Feb 2023 ZhiGang Liu, Xin Luo

Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning.

Community Detection Representation Learning

Constraint-Induced Symmetric Nonnegative Matrix Factorization for Accurate Community Detection

1 code implementation journal 2023 ZhiGang Liu, Xin Luo, Zidong Wang, Xiaohui Liu

Motivated by this discovery, this paper proposes a novel Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent feature matrices, thus preserving its representation learning capacity; b) Incorporating a symmetry-regularizer into its objective function, which preserves the symmetry of the learnt low-rank approximation to the adjacency matrix, thereby making the resultant detector precisely illustrate the target network’s symmetry; and c) Introducing a graph-regularizer that preserves local invariance of the network’s intrinsic geometry into its learning objective, thus making the achieved detector well-aware of community structure within the target network.

Community Detection Representation Learning

Multi-queue Momentum Contrast for Microvideo-Product Retrieval

1 code implementation22 Dec 2022 Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie

The booming development and huge market of micro-videos bring new e-commerce channels for merchants.

Representation Learning Retrieval

Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction

no code implementations20 Dec 2022 Cheng Liang, Teng Huang, Yi He, Song Deng, Di wu, Xin Luo

The idea of the proposed MMA is mainly two-fold: 1) apply different $L_p$-norm on loss function and regularization to form different variant models in different metric spaces, and 2) aggregate these variant models.

Recommendation Systems

Surrogate-assisted level-based learning evolutionary search for heat extraction optimization of enhanced geothermal system

no code implementations15 Dec 2022 Guodong Chen, Xin Luo, Chuanyin Jiang, Jiu Jimmy Jiao

To solve this issue, a novel surrogate-assisted level-based learning evolutionary search algorithm (SLLES) is proposed for heat extraction optimization of enhanced geothermal system.

Management

A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs

no code implementations30 Nov 2022 Ying Wang, Ye Yuan, Xin Luo

Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG.

Model Optimization Representation Learning

A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images

1 code implementation27 Nov 2022 Wei Chen, Chen Li, Dan Chen, Xin Luo

Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality.

Contrastive Learning Image Restoration +2

Generic Cryo-CMOS Device Modeling and EDACompatible Platform for Reliable Cryogenic IC Design

no code implementations10 Nov 2022 Zhidong Tang, Zewei Wang, Yumeng Yuan, Chang He, Xin Luo, Ao Guo, Renhe Chen, Yongqi Hu, Longfei Yang, Chengwei Cao, Linlin Liu, Liujiang Yu, Ganbing Shang, Yongfeng Cao, Shoumian Chen, Yuhang Zhao, Shaojian Hu, Xufeng Kou

Furthermore, by incorporating the Cryo-CMOS compact model into the process design kit (PDK), the cryogenic 4 Kb SRAM, 5-bit flash ADC and 8-bit current steering DAC are designed, and their performance is readily investigated and optimized on the EDA-compatible platform, hence laying a solid foundation for large-scale cryogenic IC design.

FedVMR: A New Federated Learning method for Video Moment Retrieval

no code implementations28 Oct 2022 Yan Wang, Xin Luo, Zhen-Duo Chen, Peng-Fei Zhang, Meng Liu, Xin-Shun Xu

As the first that is explored in VMR field, the new task is defined as video moment retrieval with distributed data.

Federated Learning Moment Retrieval +1

Prototype-Based Layered Federated Cross-Modal Hashing

no code implementations27 Oct 2022 Jiale Liu, Yu-Wei Zhan, Xin Luo, Zhen-Duo Chen, Yongxin Wang, Xin-Shun Xu

And due to the problems of statistical heterogeneity, model heterogeneity, and forcing each client to accept the same parameters, applying federated learning to cross-modal hash learning becomes very tricky.

Personalized Federated Learning

Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization

no code implementations7 Jun 2022 Guodong Chen, Xin Luo, Jimmy Jiu Jiao, Xiaoming Xue

In this work, a novel and efficient data-driven evolutionary algorithm, called generalized data-driven differential evolutionary algorithm (GDDE), is proposed to reduce the number of simulation runs on well-placement and control optimization problems.

PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis

no code implementations5 May 2022 Ye Yuan, Xin Luo

A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes.

Computational Efficiency Representation Learning

A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data

no code implementations16 Apr 2022 Di wu, Peng Zhang, Yi He, Xin Luo

High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications.

Representation Learning

Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices

no code implementations16 Apr 2022 Di wu, Yi He, Xin Luo

A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system.

Representation Learning Vocal Bursts Intensity Prediction

Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval

no code implementations12 Apr 2022 Yu-Wei Zhan, Xin Luo, Yongxin Wang, Zhen-Duo Chen, Xin-Shun Xu

To narrow the domain differences between sketches and images, we extract edge maps for natural images and treat them as a bridge between images and sketches, which have similar content to images and similar style to sketches.

Retrieval Sketch-Based Image Retrieval

An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model

no code implementations11 Apr 2022 Yurong Zhong, Xin Luo

An alternating-direction-method-based nonnegative latent factor model can perform efficient representation learning to a high-dimensional and incomplete (HDI) matrix.

Representation Learning

A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data

no code implementations2 Apr 2022 Jia Chen, Di wu, Xin Luo

High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications.

Position

Adaptive Divergence-based Non-negative Latent Factor Analysis

no code implementations30 Mar 2022 Ye Yuan, Guangxiao Yuan, Renfang Wang, Xin Luo

High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative for representing the inherent non-negativity of node interactions.

Computational Efficiency

ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator

no code implementations24 Mar 2022 Zi-Chao Zhang, Zhen-Duo Chen, Yongxin Wang, Xin Luo, Xin-Shun Xu

Recently, several Vision Transformer (ViT) based methods have been proposed for Fine-Grained Visual Classification (FGVC). These methods significantly surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC tasks. However, there are some limitations when applying ViT directly to FGVC. First, ViT needs to split images into patches and calculate the attention of every pair, which may result in heavy redundant calculation and unsatisfying performance when handling fine-grained images with complex background and small objects. Second, a standard ViT only utilizes the class token in the final layer for classification, which is not enough to extract comprehensive fine-grained information.

Fine-Grained Image Classification

High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection

no code implementations8 Mar 2022 ZhiGang Liu, Xin Luo

Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circle.

Community Detection

Second-order Symmetric Non-negative Latent Factor Analysis

no code implementations4 Mar 2022 Weiling Li, Xin Luo

Precise representation of large-scale undirected network is the basis for understanding relations within a massive entity set.

Asymmetric Modality Translation For Face Presentation Attack Detection

no code implementations18 Oct 2021 Zhi Li, Haoliang Li, Xin Luo, Yongjian Hu, Kwok-Yan Lam, Alex C. Kot

In this paper, we propose a novel framework based on asymmetric modality translation for face presentation attack detection in bi-modality scenarios.

Face Presentation Attack Detection Face Recognition +1

Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data

1 code implementation9 Sep 2021 Xiao-Ming Wu, Xin Luo, Yu-Wei Zhan, Chen-Lu Ding, Zhen-Duo Chen, Xin-Shun Xu

With the vigorous development of multimedia equipment and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic.

Retrieval

Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation

no code implementations23 Jun 2021 Xin Luo, Wei Chen, Yusong Tan, Chen Li, Yulin He, Xiaogang Jia

It is desirable to transfer the knowledge stored in a well-trained source model onto non-annotated target domain in the absence of source data.

Pseudo Label Segmentation +2

Generalized Nesterov's Acceleration-incorporated Non-negative and Adaptive Latent Factor Analysis

no code implementations IEEE Transactions on Services Computing 2021 Xin Luo, Yue Zhou, ZhiGang Liu, Lun Hu, Mengchu Zhou

A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service applications.

Computational Efficiency

Far-Field Super-Resolution Imaging By Nonlinear Excited Evanescent Waves

no code implementations14 Jan 2021 ZhiHao Zhou, Wei Liu, Jiajing He, Lei Chen, Xin Luo, Dongyi Shen, Jianjun Cao, Yaping Dan, Xianfeng Chen, Wenjie Wan

Abbe's resolution limit, one of the best-known physical limitations, poses a great challenge for any wave systems in imaging, wave transport, and dynamics.

Super-Resolution Optics

Weakly-Supervised Online Hashing

no code implementations16 Sep 2020 Yu-Wei Zhan, Xin Luo, Yu Sun, Yongxin Wang, Zhen-Duo Chen, Xin-Shun Xu

However, existing hashing methods for social image retrieval are based on batch mode which violates the nature of social images, i. e., social images are usually generated periodically or collected in a stream fashion.

Image Retrieval Retrieval

Dual Graph Representation Learning

no code implementations25 Feb 2020 Huiling Zhu, Xin Luo, Hankz Hankui Zhuo

Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications.

Graph Representation Learning

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