94 citations · 261 across the 13 of their papers we have counts for
13 papers
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
Zijie Huang, Daheng Wang, Binxuan Huang +8
Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact ma…
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Enyan Dai, Limeng Cui, Zhengyang Wang +5
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can foo…
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Yaochen Xie, Zhao Xu, Jingtun Zhang +2
Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new…
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Zhengyang Wang, Meng Liu, Youzhi Luo +8
Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predict…
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Yaochen Xie, Zhengyang Wang, Shuiwang Ji
Self-supervised frameworks that learn denoising models with merely individual noisy images have shown strong capability and promising performance in various image denoising tasks.…
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
Lei Cai, Zhengyang Wang, Rob Kulathinal +2
Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene inte…
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage
Zhengyang Wang, Bunyamin Sisman, Hao Wei +2
Entity linkage (EL) is a critical problem in data cleaning and integration. In the past several decades, EL has typically been done by rule-based systems or traditional machine lea…
Global Voxel Transformer Networks for Augmented Microscopy
Zhengyang Wang, Yaochen Xie, Shuiwang Ji
Advances in deep learning have led to remarkable success in augmented microscopy, enabling us to obtain high-quality microscope images without using expensive microscopy hardware a…
Second-Order Pooling for Graph Neural Networks
Zhengyang Wang, Shuiwang Ji
Graph neural networks have achieved great success in learning node representations for graph tasks such as node classification and link prediction. Graph representation learning re…
Kronecker Attention Networks
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Attention operators have been applied on both 1-D data like texts and higher-order data such as images and videos. Use of attention operators on high-order data requires flattening…
iCapsNets: Towards Interpretable Capsule Networks for Text Classification
Zhengyang Wang, Xia Hu, Shuiwang Ji
Many text classification applications require models with satisfying performance as well as good interpretability. Traditional machine learning methods are easy to interpret but ha…
Non-Local Graph Neural Networks
Meng Liu, Zhengyang Wang, Shuiwang Ji
Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on di…
Non-local U-Net for Biomedical Image Segmentation
Zhengyang Wang, Na Zou, Dinggang Shen +1
Deep learning has shown its great promise in various biomedical image segmentation tasks. Existing models are typically based on U-Net and rely on an encoder-decoder architecture w…