Search Results for author: Qing Liao

Found 16 papers, 6 papers with code

DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning

no code implementations14 Mar 2024 Xu Yang, Jiyuan Feng, Songyue Guo, Ye Wang, Ye Ding, Binxing Fang, Qing Liao

In this paper, we propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem during federated learning.

Personalized Federated Learning

FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling

1 code implementation5 Mar 2024 Hongyu Zhang, Dongyi Zheng, Lin Zhong, Xu Yang, Jiyuan Feng, Yunqing Feng, Qing Liao

Specifically, to address the data heterogeneity across domains, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features.

Contrastive Learning Data Augmentation +7

FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning

1 code implementation15 Sep 2023 Hongyu Zhang, Dongyi Zheng, Xu Yang, Jiyuan Feng, Qing Liao

Nonetheless, the sequence feature heterogeneity across different domains significantly impacts the overall performance of FL.

Data Augmentation Disentanglement +3

Unbiased Scene Graph Generation via Two-stage Causal Modeling

no code implementations11 Jul 2023 Shuzhou Sun, Shuaifeng Zhi, Qing Liao, Janne Heikkilä, Li Liu

To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages.

Causal Inference Graph Generation +2

Unpaired Multi-View Graph Clustering with Cross-View Structure Matching

1 code implementation7 Jul 2023 Yi Wen, Siwei Wang, Qing Liao, Weixuan Liang, Ke Liang, Xinhang Wan, Xinwang Liu

Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering.

Clustering Graph Clustering

LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types

1 code implementation12 Jun 2023 Xin-Cheng Wen, Cuiyun Gao, Feng Luo, Haoyu Wang, Ge Li, Qing Liao

(2) adaptive re-weighting module, which adjusts the learning weights for different types according to the training epochs and numbers of associated samples by a novel training loss.

Classification Graph Neural Network +2

Deep Intellectual Property Protection: A Survey

no code implementations28 Apr 2023 Yuchen Sun, Tianpeng Liu, Panhe Hu, Qing Liao, Shaojing Fu, Nenghai Yu, Deke Guo, Yongxiang Liu, Li Liu

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields.

RARE: Robust Masked Graph Autoencoder

no code implementations4 Apr 2023 Wenxuan Tu, Qing Liao, Sihang Zhou, Xin Peng, Chuan Ma, Zhe Liu, Xinwang Liu, Zhiping Cai

To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space.

Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation

no code implementations28 Sep 2022 Xinni Zhang, Yankai Chen, Cuiyun Gao, Qing Liao, Shenglin Zhao, Irwin King

Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention.

Knowledge Graphs

Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

1 code implementation25 Jul 2022 Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, Guansong Pang

To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration.

One-Class Classification One-class classifier +2

On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation

no code implementations12 Apr 2022 Wenju Zhang, Xiang Zhang, Qing Liao, Long Lan, Mengzhu Wang, Wei Wang, Baoyun Peng, Zhengming Ding

Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme.

Image Classification Unsupervised Domain Adaptation

CGNN: Traffic Classification with Graph Neural Network

no code implementations19 Oct 2021 Bo Pang, Yongquan Fu, Siyuan Ren, Ye Wang, Qing Liao, Yan Jia

Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification.

Classification Graph Neural Network +2

Pixel Difference Networks for Efficient Edge Detection

2 code implementations ICCV 2021 Zhuo Su, Wenzhe Liu, Zitong Yu, Dewen Hu, Qing Liao, Qi Tian, Matti Pietikäinen, Li Liu

A faster version of PiDiNet with less than 0. 1M parameters can still achieve comparable performance among state of the arts with 200 FPS.

Edge Detection

Realization of exciton-mediated optical spin-orbit interaction in organic microcrystalline resonators

no code implementations24 Feb 2021 Jiahuan Ren, Qing Liao, Xuekai Ma, Stefan Schumacher, Jiannian Yao, Hongbing Fu

The ability to control the spin-orbit interaction of light in optical microresonators is of fundamental importance for future photonics.

Optics Mesoscale and Nanoscale Physics

General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme

no code implementations NeurIPS 2019 Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao

In this paper, we propose a general proximal incremental aggregated gradient algorithm, which contains various existing algorithms including the basic incremental aggregated gradient method.

Cannot find the paper you are looking for? You can Submit a new open access paper.