1 code implementation • 10 May 2024 • Ziyao Li
This short paper is a fast proof-of-concept that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be well approximated by Gaussian radial basis functions.
no code implementations • NeurIPS 2021 • Shuwen Yang, Ziyao Li, Guojie Song, Lingsheng Cai
To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules.
1 code implementation • 13 Nov 2021 • Shuwen Yang, Tianyu Wen, Ziyao Li, Guojie Song
Straight-forward conformation generation models, which generate 3-D structures directly from input molecular graphs, play an important role in various molecular tasks with machine learning, such as 3D-QSAR and virtual screening in drug design.
1 code implementation • 8 May 2021 • Ziyao Li, Shuwen Yang, Guojie Song, Lingsheng Cai
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
no code implementations • ICLR 2021 • Ziyao Li, Shuwen Yang, Guojie Song, Lingsheng Cai
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
no code implementations • 1 Jan 2021 • Xiaojun Ma, Ziyao Li, Lingjun Xu, Guojie Song, Yi Li, Chuan Shi
To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs).
no code implementations • 4 Dec 2020 • Junshan Wang, Ziyao Li, Qingqing Long, Weiyu Zhang, Guojie Song, Chuan Shi
Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting.
no code implementations • 15 Jul 2019 • Zheng Liu, Yu Xing, Jianxun Lian, Defu Lian, Ziyao Li, Xing Xie
Our work is undergoing a anonymous review, and it will soon be released after the notification.
1 code implementation • 26 Feb 2019 • Ziyao Li, Liang Zhang, Guojie Song
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes.
no code implementations • 14 Nov 2018 • Ziyao Li, Liang Zhang, Guojie Song
We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes.