Search Results for author:
Found 16 papers, 6 papers with code
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
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.
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
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.
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning
Nonetheless, the sequence feature heterogeneity across different domains significantly impacts the overall performance of FL.
Unbiased Scene Graph Generation via Two-stage Causal Modeling
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.
Unpaired Multi-View Graph Clustering with Cross-View Structure Matching
Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering.
LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types
(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.
Deep Intellectual Property Protection: A Survey
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
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.
Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis
In this work, we propose a novel Syntax-guided Domain Adaptation Model, named SDAM, for more effective cross-domain ABSA.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention.
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Our one-class classifier is calibrated in two ways: (1) by adaptively penalizing uncertain predictions, which helps eliminate the impact of anomaly contamination while accentuating the predictions that the one-class model is confident in, and (2) by discriminating the normal samples from native anomaly examples that are generated to simulate genuine time series abnormal behaviors on the basis of original data.
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme.
CGNN: Traffic Classification with Graph Neural Network
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.
Pixel Difference Networks for Efficient Edge Detection
A faster version of PiDiNet with less than 0. 1M parameters can still achieve comparable performance among state of the arts with 200 FPS.
Ranked #2 on
Edge Detection
on BRIND
Realization of exciton-mediated optical spin-orbit interaction in organic microcrystalline resonators
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
In this paper, we propose a general proximal incremental aggregated gradient algorithm, which contains various existing algorithms including the basic incremental aggregated gradient method.
