515 citations · 575 across the 10 of their papers we have counts for
9 papers · 1 filter
On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods
Montgomery Bohde, Meng Liu, Alexandra Saxton +1
Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in exist…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Graph Mixup with Soft Alignments
Hongyi Ling, Zhimeng Jiang, Meng Liu +2
We study graph data augmentation by mixup, which has been used successfully on images. A key operation of mixup is to compute a convex combination of a pair of inputs. This operati…
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
Shurui Gui, Meng Liu, Xiner Li +2
We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment informat…
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences
Meng Liu, Shuiwang Ji
Modern graph neural networks (GNNs) use a message passing scheme and have achieved great success in many fields. However, this recursive design inherently leads to excessive comput…
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Zhao Xu, Youzhi Luo, Xuan Zhang +7
Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies…