34 citations · 116 across the 13 of their papers we have counts for
15 papers · 1 filter
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
Tong Wei, Qian-Yu Liu, Jiang-Xin Shi +2
Long-Tailed Semi-Supervised Learning (LTSSL) aims to learn from class-imbalanced data where only a few samples are annotated. Existing solutions typically require substantial cost…
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Zhen Xu, Lanning Wei, Huan Zhao +4
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in mode…
LTU Attacker for Membership Inference
Joseph Pedersen, Rafael Muñoz-Gómez, Jiangnan Huang +3
We address the problem of defending predictive models, such as machine learning classifiers (Defender models), against membership inference attacks, in both the black-box and white…
Robust Long-Tailed Learning under Label Noise
Tong Wei, Jiang-Xin Shi, Wei-Wei Tu +1
Long-tailed learning has attracted much attention recently, with the goal of improving generalisation for tail classes. Most existing works use supervised learning without consider…
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Xiawei Guo, Yuhan Quan, Huan Zhao +3
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing wo…
Search to aggregate neighborhood for graph neural network
Huan Zhao, Quanming Yao, Weiwei Tu
Recent years have witnessed the popularity and success of graph neural networks (GNN) in various scenarios. To obtain data-specific GNN architectures, researchers turn to neural ar…