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Found 9 papers, 3 papers with code
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces
To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE.
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
In light of the issues above, we propose the problem of \emph{Motif-aware Riemannian Graph Representation Learning}, seeking a numerically stable encoder to capture motif regularity in a diverse-curvature manifold without labels.
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection
Image reconstruction-based anomaly detection models are widely explored in industrial visual inspection.
Ranked #6 on
Anomaly Detection
on VisA
SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds
To explore these issues for sequential interaction networks, we propose SINCERE, a novel method representing Sequential Interaction Networks on Co-Evolving RiEmannian manifolds.
Contrastive Graph Clustering in Curvature Spaces
On the other hand, contrastive learning boosts the deep graph clustering but usually struggles in either graph augmentation or hard sample mining.
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence.
Data-driven Smart Ponzi Scheme Detection
To solve these problems, we propose a data-driven smart Ponzi scheme detection system in this paper.
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.
Exploiting Multiple Embeddings for Chinese Named Entity Recognition
Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level.
Chinese Named Entity Recognition
named-entity-recognition
+2
