no code implementations • 16 May 2024 • Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur Barnes, Russell Bent
The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts.
no code implementations • 10 Apr 2024 • Ningfeng Liu, Jie Yu, Siyu Xiu, Xinfang Zhao, Siyu Lin, Bo Qiang, Ruqiu Zheng, Hongwei Jin, Liangren Zhang, Zhenming Liu
Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology.
no code implementations • 2 Oct 2023 • Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash
To address this problem, we introduce an autoencoder-driven self-supervised learning~(SSL) approach that learns a summary statistic from unlabeled workflow data and estimates the normal behavior of the computational workflow in the latent space.
1 code implementation • 12 May 2022 • Hongwei Jin, Zishun Yu, Xinhua Zhang
Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e. g., graph classification.
no code implementations • 1 Feb 2022 • Hongwei Jin, Xun Chen
Learning the similarity between structured data, especially the graphs, is one of the essential problems.
1 code implementation • NeurIPS 2020 • Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang
Graph convolution networks (GCNs) have become effective models for graph classification.
1 code implementation • 24 Dec 2019 • Yanxing Wang, Jianxing Hu, Junyong Lai, Yibo Li, Hongwei Jin, Lihe Zhang, Liangren Zhang, Zhenming Liu
Molecular fingerprints are the workhorse in ligand-based drug discovery.