137 citations · 255 across the 9 of their papers we have counts for
19 papers
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…
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task
Jiuhai Chen, Chen Zhu, Bin Dai
Self-supervised learning (SSL) has achieved great success in a variety of computer vision tasks. However, the mechanism of how SSL works in these tasks remains a mystery. In this p…
The Intrinsic Dimension of Images and Its Impact on Learning
Phillip Pope, Chen Zhu, Ahmed Abdelkader +2
It is widely believed that natural image data exhibits low-dimensional structure despite the high dimensionality of conventional pixel representations. This idea underlies a common…
Modifying Memories in Transformer Models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer +4
Large Transformer models have achieved impressive performance in many natural language tasks. In particular, Transformer based language models have been shown to have great capabil…
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks
Huimin Zeng, Chen Zhu, Tom Goldstein +1
Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks. However, traditional d…
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer
Chen Zhu, Zheng Xu, Ali Shafahi +3
When large scale training data is available, one can obtain compact and accurate networks to be deployed in resource-constrained environments effectively through quantization and p…