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08/04/2023
VQGraph: Graph Vector-Quantization for Bridging GNNs and MLPs
Graph Neural Networks (GNNs) conduct message passing which aggregates lo...
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06/28/2023
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization
Out-of-distribution (OOD) graph generalization are critical for many rea...
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11/06/2022
The Importance of Suppressing Complete Reconstruction in Autoencoders for Unsupervised Outlier Detection
Autoencoders are widely used in outlier detection due to their superiori...
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09/02/2022
Diffusion Models: A Comprehensive Survey of Methods and Applications
Diffusion models are a class of deep generative models that have shown i...
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05/31/2022
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Unsupervised/self-supervised graph representation learning is critical f...
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05/20/2022
Cross Reconstruction Transformer for Self-Supervised Time Series Representation Learning
Unsupervised/self-supervised representation learning in time series is c...
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05/19/2022
Spatial Autoregressive Coding for Graph Neural Recommendation
Graph embedding methods including traditional shallow models and deep Gr...
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02/08/2022
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Unsupervised/self-supervised time series representation learning is a ch...
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02/08/2022
Spectral Propagation Graph Network for Few-shot Time Series Classification
Few-shot Time Series Classification (few-shot TSC) is a challenging prob...
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03/31/2020
DPGN: Distribution Propagation Graph Network for Few-shot Learning
We extend this idea further to explicitly model the distribution-level r...
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04/16/2019
