21 citations · 36 across the 9 of their papers we have counts for
10 papers
Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts
Yue Hou, Ruomei Liu, Yingke Su +2
Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution drifts continually over time.…
DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home
Changshuo Liu, Junran Wu, Zhongle Xie +9
Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices, which limits their accessibility and potential for health management outside clini…
NC-NCD: Novel Class Discovery for Node Classification
Yue Hou, Xueyuan Chen, He Zhu +5
Novel Class Discovery (NCD) involves identifying new categories within unlabeled data by utilizing knowledge acquired from previously established categories. However, existing NCD…
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
Jiaqi Zhu, Shaofeng Cai, Fang Deng +2
Large vision-language models (LVLMs) are markedly proficient in deriving visual representations guided by natural language. Recent explorations have utilized LVLMs to tackle zero-s…
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification
He Zhu, Junran Wu, Ruomei Liu +5
Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extr…
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
He Zhu, Chong Zhang, Junjie Huang +2
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in…
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
Junran Wu, Xueyuan Chen, Bowen Shi +2
In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contra…
Structural Entropy Guided Graph Hierarchical Pooling
Junran Wu, Xueyuan Chen, Ke Xu +1
Following the success of convolution on non-Euclidean space, the corresponding pooling approaches have also been validated on various tasks regarding graphs. However, because of th…
A Simple yet Effective Method for Graph Classification
Junran Wu, Shangzhe Li, Jianhao Li +2
In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to bo…
Hierarchical information matters: Text classification via tree based graph neural network
Chong Zhang, He Zhu, Xingyu Peng +2
Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification ta…