Qingtian Bian

Fine-Grained Disentanglement for Alleviating Inconsistencies in Cross-Modal Hashing Retrieval

Tieying Li, Xiaochun Yang, Bin Wang, Lingdu Kong et autres

Cross-modal hashing retrieval faces fundamental challenges from modality-modality (M-M) and modality-label (M-L) inconsistencies inherent in multimodal data. Existing methods rely on coarse-grained disentanglement to address these inconsistencies, but suffer from inaccurate semantic separation and modality-common semantic information loss during cross-modal alignment. Through …

cn, sg (code pays fourni par la source)

Multi-Domain Enhancement via Residual Interwoven Transfer in Cross-Domain Sequential Recommendation

Qingtian Bian, Tieying Li, Marcus de Carvalho, Jiaxing Xu et autres

To mitigate data sparsity in Sequential Recommendation, Cross-Domain Sequential Recommendation (CDSR) exploits dynamic knowledge transfer across domains. Traditional CDSR approaches merge specific-domain sequences into mixed-domain sequences to reconnect users' dispersed interests. However, most methods rely on unidirectional transfer between mixed and specific …

sg, cn (code pays fourni par la source)

Multi-Atlas Brain Network Classification Through Consistency Distillation and Complementary Information Fusion

Jiaxing Xu, Mengcheng Lan, Xia Dong, Kai He et autres

Brain network analysis plays a crucial role in identifying distinctive patterns associated with neurological disorders. Functional magnetic resonance imaging (fMRI) enables the construction of brain networks by analyzing correlations in blood-oxygen-level-dependent (BOLD) signals across different brain regions, known as regions of interest …

sg, fr (code pays fourni par la source)

ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation

Qingtian Bian, Marcus de Carvalho, Tieying Li, Jiaxing Xu et autres

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains.A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains.One key challenge is to correctly extract the shared knowledge among these …

sg, cn (code pays fourni par la source)

Corrections to “Contrastive Graph Pooling for Explainable Classification of Brain Networks”

Jiaxing Xu, Qingtian Bian, Xinhang Li, A.X.J. Zhang et autres

sg, cn, nz, se (code pays fourni par la source)

Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification

Jiaxing Xu, Kai He, Mengcheng Lan, Qingtian Bian et autres

Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging (fMRI) data. Despite the prevalence of Graph Neural Networks (GNNs) and Graph Transformers in various domains, applying them to …

sg, cn, nz (code pays fourni par la source)

Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion

Jiaxing Xu, Mengcheng Lan, Dong Xia, Kai He et autres

In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance imaging (fMRI) serves as a primary tool for mapping these networks by correlating blood-oxygen-level-dependent (BOLD) signals across different brain regions, defined …

Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification

Jiaxing Xu, Kai He, Mengcheng Lan, Qingtian Bian et autres

Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging (fMRI) data. Despite the prevalence of Graph Neural Networks (GNNs) and Graph Transformers in various domains, applying them to …

Contrastive Graph Pooling for Explainable Classification of Brain Networks

Jiaxing Xu, Qingtian Bian, Xinhang Li, A.X.J. Zhang et autres

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a graph …

sg, cn, nz, se (code pays fourni par la source)

Union Subgraph Neural Networks

Jiaxing Xu, Aihu Zhang, Qingtian Bian, Vijay Prakash Dwivedi et autres

Graph Neural Networks (GNNs) are widely used for graph representation learning in many application domains. The expressiveness of vanilla GNNs is upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL) test as they operate on rooted subtrees through iterative message passing. In this paper, we empower …

sg (code pays fourni par la source)

CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning

Qingtian Bian, Jiaxing Xu, Hui Fang, Yiping Ke

The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. However, they …

sg, cn (code pays fourni par la source)

Contrastive Graph Pooling for Explainable Classification of Brain Networks

Jiaxing Xu, Qingtian Bian, Xinhang Li, Aihu Zhang et autres

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a graph …