3mformer: Multi-order multi-mode transformer for skeletal action recognition
Many skeletal action recognition models use GCNs to represent the human body by 3D
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …
Mitigating the popularity bias of graph collaborative filtering: A dimensional collapse perspective
Abstract Graph-based Collaborative Filtering (GCF) is widely used in personalized
recommendation systems. However, GCF suffers from a fundamental problem where …
recommendation systems. However, GCF suffers from a fundamental problem where …
Optimal block-wise asymmetric graph construction for graph-based semi-supervised learning
Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …
underlying manifold structures of samples in high-dimensional spaces. It involves two …
Hyperbolic representation learning: Revisiting and advancing
The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable
attention in the realm of representation learning. Current endeavors in hyperbolic …
attention in the realm of representation learning. Current endeavors in hyperbolic …
Contrastive cross-scale graph knowledge synergy
Graph representation learning via Contrastive Learning (GCL) has drawn considerable
attention recently. Efforts are mainly focused on gathering more global information via …
attention recently. Efforts are mainly focused on gathering more global information via …
Bipartite graph convolutional hashing for effective and efficient top-n search in hamming space
Searching on bipartite graphs is basal and versatile to many real-world Web applications,
eg, online recommendation, database retrieval, and query-document searching. Given a …
eg, online recommendation, database retrieval, and query-document searching. Given a …
A survey of trustworthy federated learning with perspectives on security, robustness and privacy
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly
benefited human society. Among various AI technologies, Federated Learning (FL) stands …
benefited human society. Among various AI technologies, Federated Learning (FL) stands …
No change, no gain: empowering graph neural networks with expected model change maximization for active learning
Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …
graph-structured data, but their success depends on sufficient labeled data. We present a …
Graph contrastive learning with stable and scalable spectral encoding
Graph contrastive learning (GCL) aims to learn representations by capturing the agreements
between different graph views. Traditional GCL methods generate views in the spatial …
between different graph views. Traditional GCL methods generate views in the spatial …
Learning partial correlation based deep visual representation for image classification
Visual representation based on covariance matrix has demonstrates its efficacy for image
classification by characterising the pairwise correlation of different channels in convolutional …
classification by characterising the pairwise correlation of different channels in convolutional …