649 citations · 2.1k across the 52 of their papers we have counts for
12 papers · 1 filter
Graph Convolutional Module for Temporal Action Localization in Videos
Runhao Zeng, Wenbing Huang, Mingkui Tan +4
Temporal action localization has long been researched in computer vision. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e.,…
Local Augmentation for Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong +6
Graph Neural Networks (GNNs) have achieved remarkable performance on graph-based tasks. The key idea for GNNs is to obtain informative representation through aggregating informatio…
Frustratingly Easy Transferability Estimation
Long-Kai Huang, Ying Wei, Yu Rong +2
Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on…
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions
Xuefeng Du, Tian Bian, Yu Rong +6
Teaching Graph Neural Networks (GNNs) to accurately classify nodes under severely noisy labels is an important problem in real-world graph learning applications, but is currently u…
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
Yatao Bian, Yu Rong, Tingyang Xu +3
Valuation problems, such as feature interpretation, data valuation and model valuation for ensembles, become increasingly more important in many machine learning applications. Such…
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
Heng Chang, Yu Rong, Tingyang Xu +6
With the success of the graph embedding model in both academic and industry areas, the robustness of graph embedding against adversarial attack inevitably becomes a crucial problem…
Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation
Jinyu Yang, Chunyuan Li, Weizhi An +5
Recent studies imply that deep neural networks are vulnerable to adversarial examples -- inputs with a slight but intentional perturbation are incorrectly classified by the network…
Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed
Yuli Jiang, Yu Rong, Hong Cheng +3
Given one or more query vertices, Community Search (CS) aims to find densely intra-connected and loosely inter-connected structures containing query vertices. Attributed Community…
Recognizing Predictive Substructures with Subgraph Information Bottleneck
Junchi Yu, Tingyang Xu, Yu Rong +3
The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack…
Diversified Multiscale Graph Learning with Graph Self-Correction
Yuzhao Chen, Yatao Bian, Jiying Zhang +4
Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering…
Learning Diverse-Structured Networks for Adversarial Robustness
Xuefeng Du, Jingfeng Zhang, Bo Han +5
In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones…
Towards Expectation-Maximization by SQL in RDBMS
Kangfei Zhao, Jeffrey Xu Yu, Yu Rong +2
Integrating machine learning techniques into RDBMSs is an important task since there are many real applications that require modeling (e.g., business intelligence, strategic analys…