activity
20162025
most citedKnowledge Distillation on Graphs: A Survey

8 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CY20251 cited

On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective

Yue Huang, Chujie Gao, Siyuan Wu +63

Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…

cs.LG20238 cited

Knowledge Distillation on Graphs: A Survey

Yijun Tian, Shichao Pei, Xiangliang Zhang +2

Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limi…

cs.SD20221 cited

A Generative deep learning approach for shape recognition of arbitrary objects from phaseless acoustic scattering data

W. W. Ahmed, M. Farhat, P. -Y. Chen +2

We propose and demonstrate a generative deep learning approach for the shape recognition of an arbitrary object from its acoustic scattering properties. The strategy exploits deep…

eess.SY2021

Data-Driven State Estimation for Light-Emitting Diode Underwater Optical Communication

Yingquan Li, Zhenwen Liang, Ibrahima N'Doye +3

Light-Emitting Diodes (LEDs) based underwater optical wireless communications (UOWCs), a technology with low latency and high data rates, have attracted significant importance for…

cs.DC2016

Analysis and Modeling of Social Influence in High Performance Computing Workloads

Shuai Zheng, Zon-Yin Shae, Xiangliang Zhang +2

Social influence among users (e.g., collaboration on a project) creates bursty behavior in the underlying high performance computing (HPC) workloads. Using representative HPC and c…