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.…
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…
Graph Out-of-Distribution Detection via Test-Time Calibration with Dual Dynamic Dictionaries
Yue Hou, Ruomei Liu, Yingke Su +2
A key challenge in graph out-of-distribution (OOD) detection lies in the absence of ground-truth OOD samples during training. Existing methods are typically optimized to capture fe…
Toward Robust Signed Graph Learning through Joint Input-Target Denoising
Junran Wu, Beng Chin Ooi, Ke Xu
Signed Graph Neural Networks (SGNNs) are widely adopted to analyze complex patterns in signed graphs with both positive and negative links. Given the noisy nature of real-world con…
Redundancy-Aware Test-Time Graph Out-of-Distribution Detection
Yue Hou, He Zhu, Ruomei Liu +3
Distributional discrepancy between training and test data can lead models to make inaccurate predictions when encountering out-of-distribution (OOD) samples in real-world applicati…
Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection
Yue Hou, He Zhu, Ruomei Liu +4
With the emerging of huge amount of unlabeled data, unsupervised out-of-distribution (OOD) detection is vital for ensuring the reliability of graph neural networks (GNNs) by identi…
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…
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…
Segment, Lift and Fit: Automatic 3D Shape Labeling from 2D Prompts
Jianhao Li, Tianyu Sun, Zhongdao Wang +7
This paper proposes an algorithm for automatically labeling 3D objects from 2D point or box prompts, especially focusing on applications in autonomous driving. Unlike previous arts…
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…