Web: http://arxiv.org/abs/2209.07924

Sept. 19, 2022, 1:11 a.m. | Xiaoqi Wang, Han-Wei Shen

cs.LG updates on arXiv.org arxiv.org

Recently, Graph Neural Networks (GNNs) have significantly advanced the
performance of machine learning tasks on graphs. However, this technological
breakthrough makes people wonder: how does a GNN make such decisions, and can
we trust its prediction with high confidence? When it comes to some critical
fields such as biomedicine, where making wrong decisions can have severe
consequences, interpreting the inner working mechanisms of GNNs before applying
them is crucial. In this paper, we propose a novel model-agnostic model-level
explanation method …

arxiv graph graph neural networks networks neural networks

More from arxiv.org / cs.LG updates on arXiv.org

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France