7 citations · 17 across the 5 of their papers we have counts for
5 papers
Concept-Based Unsupervised Domain Adaptation
Xinyue Xu, Yueying Hu, Hui Tang +4
Concept Bottleneck Models (CBMs) enhance interpretability by explaining predictions through human-understandable concepts but typically assume that training and test data share the…
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
Xinyue Xu, Yi Qin, Lu Mi +2
Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work…
Subgraph Frequency Distribution Estimation using Graph Neural Networks
Zhongren Chen, Xinyue Xu, Shengyi Jiang +2
Small subgraphs (graphlets) are important features to describe fundamental units of a large network. The calculation of the subgraph frequency distributions has a wide application…
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework
Lu Mi, Tianxing He, Core Francisco Park +3
Latent-space interpolation is commonly used to demonstrate the generalization ability of deep latent variable models. Various algorithms have been proposed to calculate the best tr…
Learning Guided Electron Microscopy with Active Acquisition
Lu Mi, Hao Wang, Yaron Meirovitch +5
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection. Datasets are typic…