no code implementations • 21 Mar 2023 • Xingjian Du, Zijie Wang, Xia Liang, Huidong Liang, Bilei Zhu, Zejun Ma
Deep learning based methods have become a paradigm for cover song identification (CSI) in recent years, where the ByteCover systems have achieved state-of-the-art results on all the mainstream datasets of CSI.
1 code implementation • 7 Nov 2022 • Huidong Liang, Xingjian Du, Bilei Zhu, Zejun Ma, Ke Chen, Junbin Gao
Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e. g., randomly adding or dropping edges and nodes).
1 code implementation • 9 Nov 2021 • Huidong Liang, Junbin Gao
This paper introduces Wasserstein Adversarially Regularized Graph Autoencoder (WARGA), an implicit generative algorithm that directly regularizes the latent distribution of node embedding to a target distribution via the Wasserstein metric.
1 code implementation • 30 Sep 2021 • Huidong Liang, Junbin Gao
Link prediction is a fundamental problem in graph data analysis.