5 citations · 16 across the 11 of their papers we have counts for
9 papers · 1 filter
SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems
Yuxuan Ren, Fan Yang, Jianhua Yao +1
Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, a…
3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding
Chang Wu, Zhiyuan Liu, Wen Shu +6
Masked graph modeling (MGM) is a promising approach for molecular representation learning (MRL).However, extending the success of re-mask decoding from 2D to 3D MGM is non-trivial,…
Learning to Learn with Contrastive Meta-Objective
Shiguang Wu, Yaqing Wang, Yatao Bian +1
Meta-learning enables learning systems to adapt quickly to new tasks, similar to humans. Different meta-learning approaches all work under/with the mini-batch episodic training fra…
How Interpretable Are Interpretable Graph Neural Networks?
Yongqiang Chen, Yatao Bian, Bo Han +1
Interpretable graph neural networks (XGNNs ) are widely adopted in various scientific applications involving graph-structured data. Existing XGNNs predominantly adopt the attention…
Graph Unitary Message Passing
Haiquan Qiu, Quanming Yao
Unitarity is a useful principle for stabilizing deep neural networks, but in graph neural networks (GNNs) instability is induced not only by learnable parameters but also by the gr…
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations
Binghui Xie, Yatao Bian, Kaiwen zhou +5
Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodolog…
Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes
Yongqiang Chen, Binghui Xie, Kaiwen Zhou +3
In-context learning (ICL) refers to the ability of a model to condition on a few in-context demonstrations (input-output examples of the underlying task) to generate the answer for…
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Yongqiang Chen, Yatao Bian, Kaiwen Zhou +3
Invariant graph representation learning aims to learn the invariance among data from different environments for out-of-distribution generalization on graphs. As the graph environme…
Learning Invariant Molecular Representation in Latent Discrete Space
Xiang Zhuang, Qiang Zhang, Keyan Ding +5
Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when dat…