7 citations · 11 across the 7 of their papers we have counts for
7 papers
Beyond the Laplacian: Interpolated Spectral Augmentation for Graph Neural Networks
Ziyao Cui, Edric Tam
Graph neural networks (GNNs) are fundamental tools in graph machine learning. The performance of GNNs relies crucially on the availability of informative node features, which can b…
On the Statistical Capacity of Deep Generative Models
Edric Tam, David B. Dunson
Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not…
Assessing the Distributional Fidelity of Synthetic Chest X-rays using the Embedded Characteristic Score
Edric Tam, Barbara E Engelhardt
Chest X-ray (CXR) images are among the most commonly used diagnostic imaging modalities in clinical practice. Stringent privacy constraints often limit the public dissemination of…
Exact Sampling of Spanning Trees via Fast-forwarded Random Walks
Edric Tam, David B. Dunson, Leo L. Duan
Tree graphs are routinely used in statistics. When estimating a Bayesian model with a tree component, sampling the posterior remains a core difficulty. Existing Markov chain Monte…
Spectral Gap Regularization of Neural Networks
Edric Tam, David Dunson
We introduce Fiedler regularization, a novel approach for regularizing neural networks that utilizes spectral/graphical information. Existing regularization methods often focus on…
Multiscale Graph Comparison via the Embedded Laplacian Discrepancy
Edric Tam, David Dunson
Laplacian eigenvectors capture natural community structures on graphs and are widely used in spectral clustering and manifold learning. The use of Laplacian eigenvectors as embeddi…
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
Edric Tam, David Dunson
We introduce a novel regularization approach for deep learning that incorporates and respects the underlying graphical structure of the neural network. Existing regularization meth…