5 citations · 16 across the 11 of their papers we have counts for
12 papers
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…
Think Consistently, Reason Efficiently: Energy-Based Calibration for Implicit Chain-of-Thought
Zhikang Chen, Sen Cui, Deheng Ye +3
Large Language Models (LLMs) have demonstrated strong reasoning capabilities through \emph{Chain-of-Thought} (CoT) prompting, which enables step-by-step intermediate reasoning. How…
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…
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation
Shuhan Guo, Yatao Bian, Ruibing Wang +3
The remarkable success of Large Language Models (LLMs) across diverse tasks has driven the research community to extend their capabilities to molecular applications. However, most…
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…
HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignment
Yongqiang Chen, Quanming Yao, Juzheng Zhang +2
Recently, there has been a surge of interest in extending the success of large language models (LLMs) from texts to molecules. Most existing approaches adopt a graph neural network…
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…