1 citations · 2 across the 7 of their papers we have counts for
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Cross-Modal Alignment via Variational Copula Modelling
Feng Wu, Tsai Hor Chan, Fuying Wang +2
Various data modalities are common in real-world applications (e.g., electronic health records, medical images and clinical notes in healthcare). It is essential to develop multimo…
Variational Polya Tree
Lu Xu, Tsai Hor Chan, Kwok Fai Lam +2
Density estimation is essential for generative modeling, particularly with the rise of modern neural networks. While existing methods capture complex data distributions, they often…
Amplifying Prominent Representations in Multimodal Learning via Variational Dirichlet Process
Tsai Hor Chan, Feng Wu, Yihang Chen +2
Developing effective multimodal fusion approaches has become increasingly essential in many real-world scenarios, such as health care and finance. The key challenge is how to prese…
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior
Tsai Hor Chan, Dora Yan Zhang, Guosheng Yin +1
Bayesian neural networks (BNNs) treat neural network weights as random variables, which aim to provide posterior uncertainty estimates and avoid overfitting by performing inference…
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs
Yushi Feng, Tsai Hor Chan, Guosheng Yin +1
Data augmentation is necessary for graph representation learning due to the scarcity and noise present in graph data. Most of the existing augmentation methods overlook the context…
Multi-task Heterogeneous Graph Learning on Electronic Health Records
Tsai Hor Chan, Guosheng Yin, Kyongtae Bae +1
Learning electronic health records (EHRs) has received emerging attention because of its capability to facilitate accurate medical diagnosis. Since the EHRs contain enriched inform…
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
Tsai Hor Chan, Kin Wai Lau, Jiajun Shen +2
Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on low-dimensional distributional…