21 citations · 35 across the 7 of their papers we have counts for
8 papers
Can Machines Really See Objects in Images? A Study Based on Syntactic Distance and Visual Self-Referential Instances
Xingyu Peng, Junran Wu, Yue Hou +9
Can a vision model truly see an object, or does it only fit surface-level visual cues? Following Wittgenstein's view that the limits of language are the limits of the world, we vie…
Molecular Graph Contrastive Learning with Line Graph
Xueyuan Chen, Shangzhe Li, Ruomei Liu +4
Trapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning (GCL) came forward. Leading contrastive learning works show two kinds of…
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning
Junran Wu, Xueyuan Chen, Shangzhe Li
Graph contrastive learning has achieved great success in pre-training graph neural networks without ground-truth labels. Leading graph contrastive learning follows the classical sc…
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…
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…
Price graphs: Utilizing the structural information of financial time series for stock prediction
Junran Wu, Ke Xu, Xueyuan Chen +2
Great research efforts have been devoted to exploiting deep neural networks in stock prediction. While long-range dependencies and chaotic property are still two major issues that…