30 citations · 213 across the 50 of their papers we have counts for
7 papers · 1 filter
TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems
Zijie Huang, Wanjia Zhao, Jingdong Gao +6
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling. Extended from purely data-driven appro…
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
Yanqiao Zhu, Jeehyun Hwang, Keir Adams +10
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are e…
Unveiling Invariances via Neural Network Pruning
Derek Xu, Yizhou Sun, Wei Wang
Invariance describes transformations that do not alter data's underlying semantics. Neural networks that preserve natural invariance capture good inductive biases and achieve super…
Generalizing Graph ODE for Learning Complex System Dynamics across Environments
Zijie Huang, Yizhou Sun, Wei Wang
Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built…
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
Zijie Huang, Daheng Wang, Binxuan Huang +8
Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact ma…
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems
Song Jiang, Zijie Huang, Xiao Luo +1
Multi-agent dynamical systems refer to scenarios where multiple units interact with each other and evolve collectively over time. To make informed decisions in multi-agent dynamica…
Bi-Level Attention Graph Neural Networks
Roshni G. Iyer, Wei Wang, Yizhou Sun
Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs). However, GNNs handling heterogeneous graph…