4 papers · 1 filter
Dynamic Model Merging Made Slim
Guodong Du, Wanyu Lin
Model merging consolidates independently fine-tuned checkpoints without joint training or access to task data. Dynamic methods retain task-specific capacity, but must divide a limi…
On optimal solutions of classical and sliced Wasserstein GANs with non-Gaussian data
Yu-Jui Huang, Hsin-Hua Shen, Yu-Chih Huang +2
The generative adversarial network (GAN) aims to approximate an unknown distribution via a parameterized neural network (NN). While GANs have been widely applied in reinforcement a…
Faithful Interpretation for Graph Neural Networks
Lijie Hu, Tianhao Huang, Lu Yu +3
Currently, attention mechanisms have garnered increasing attention in Graph Neural Networks (GNNs), such as Graph Attention Networks (GATs) and Graph Transformers (GTs). It is not…
DPGAN: A Dual-Path Generative Adversarial Network for Missing Data Imputation in Graphs
Xindi Zheng, Yuwei Wu, Yu Pan +3
Missing data imputation poses a paramount challenge when dealing with graph data. Prior works typically are based on feature propagation or graph autoencoders to address this issue…