5 citations · 16 across the 11 of their papers we have counts for
6 papers · 1 filter
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…