553 citations · 3.2k across the 83 of their papers we have counts for
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Explainability in Graph Neural Networks: A Taxonomic Survey
Hao Yuan, Haiyang Yu, Shurui Gui +1
Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpreta…
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…
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
Xinyi Xu, Zhengyang Wang, Cheng Deng +2
Grouping has been commonly used in deep metric learning for computing diverse features. However, current methods are prone to overfitting and lack interpretability. In this work, w…
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction
Yaochen Xie, Yu Ding, Shuiwang Ji
It is time-consuming and expensive to take high-quality or high-resolution electron microscopy (EM) and fluorescence microscopy (FM) images. Taking these images could be even invas…
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…