4 papers
Causal-aware Graph Neural Architecture Search under Distribution Shifts
Peiwen Li, Xin Wang, Zeyang Zhang +5
Graph NAS has emerged as a promising approach for autonomously designing GNN architectures by leveraging the correlations between graphs and architectures. Existing methods fail to…
Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
Lei Chen, Yuan Meng, Chen Tang +5
Recent advancements in diffusion models, particularly the architectural transformation from UNet-based models to Diffusion Transformers (DiTs), significantly improve the quality an…
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
Beini Xie, Heng Chang, Ziwei Zhang +5
Graph Neural Architecture Search (GNAS) has achieved superior performance on various graph-structured tasks. However, existing GNAS studies overlook the applications of GNAS in res…
RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
Peiwen Li, Xin Wang, Zeyang Zhang +6
In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstr…