116 citations · 267 across the 9 of their papers we have counts for
10 papers · 1 filter
Graph Contrastive Learning with Augmentations
Yuning You, Tianlong Chen, Yongduo Sui +3
Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been develop…
Image Augmentations for GAN Training
Zhengli Zhao, Zizhao Zhang, Ting Chen +2
Data augmentations have been widely studied to improve the accuracy and robustness of classifiers. However, the potential of image augmentation in improving GAN models for image sy…
Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky +2
One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Althou…
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi +1
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms wit…
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
Ziniu Hu, Changjun Fan, Ting Chen +2
Graph neural networks (GNNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GNN model requires a large collection of label…
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Ting Chen, Song Bian, Yizhou Sun
Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of u…