1 citations · 1 across the 6 of their papers we have counts for
6 papers
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness
Jianpeng Liao, Jun Yan, Qian Tao
Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing m…
Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph
Song Yu, Shufeng Gong, Yanfeng Zhang +7
Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, in…
LON-GNN: Spectral GNNs with Learnable Orthonormal Basis
Qian Tao, Zhen Wang, Wenyuan Yu +2
In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node…
PHONEix: Acoustic Feature Processing Strategy for Enhanced Singing Pronunciation with Phoneme Distribution Predictor
Yuning Wu, Jiatong Shi, Tao Qian +2
Singing voice synthesis (SVS), as a specific task for generating the vocal singing voice from a music score, has drawn much attention in recent years. SVS faces the challenge that…
Sampling Gaussian Stationary Random Fields: A Stochastic Realization Approach
Bin Zhu, Jiahao Liu, Zhengshou Lai +1
Generating large-scale samples of stationary random fields is of great importance in the fields such as geomaterial modeling and uncertainty quantification. Traditional methodologi…
Muskits: an End-to-End Music Processing Toolkit for Singing Voice Synthesis
Jiatong Shi, Shuai Guo, Tao Qian +9
This paper introduces a new open-source platform named Muskits for end-to-end music processing, which mainly focuses on end-to-end singing voice synthesis (E2E-SVS). Muskits suppor…