11 citations · 16 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 11 cited
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Mucong Ding, Kezhi Kong, Jingling Li +4
Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To sca…
cs.LG2021
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy
Chen Chen, Kezhi Kong, Peihong Yu +3
Randomized smoothing (RS) is an effective and scalable technique for constructing neural network classifiers that are certifiably robust to adversarial perturbations. Most RS works…
cs.LG2020★ 5 cited
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Hao-Zhe Feng, Kezhi Kong, Minghao Chen +3
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference r…