152 citations · 231 across the 12 of their papers we have counts for
7 papers · 1 filter
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Ling Yang, Zhilin Huang, Yang Song +6
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and the…
Diffusion Models: A Comprehensive Survey of Methods and Applications
Ling Yang, Zhilong Zhang, Yang Song +6
Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation,…
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Ling Yang, Shenda Hong
Unsupervised/self-supervised graph representation learning is critical for downstream node- and graph-level classification tasks. Global structure of graphs helps discriminating re…
Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer
Wenrui Zhang, Ling Yang, Shijia Geng +1
Unsupervised/self-supervised representation learning in time series is critical since labeled samples are usually scarce in real-world scenarios. Existing approaches mainly leverag…
Spatial Autoregressive Coding for Graph Neural Recommendation
Jiayi Zheng, Ling Yang, Heyuan Wang +4
Graph embedding methods including traditional shallow models and deep Graph Neural Networks (GNNs) have led to promising applications in recommendation. Nevertheless, shallow model…
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Ling Yang, Shenda Hong
Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations. Existing works mainly adopt the fr…
Spectral Propagation Graph Network for Few-shot Time Series Classification
Ling Yang, Shenda Hong, Luxia Zhang
Few-shot Time Series Classification (few-shot TSC) is a challenging problem in time series analysis. It is more difficult to classify when time series of the same class are not com…