376 citations
- Chinese Academy of SciencesCN157 papers
- University of Chinese Academy of SciencesCN103 papers
- Beijing Academy of Artificial IntelligenceCN51 papers
- Shandong Institute of AutomationCN50 papers
- Center for Excellence in Brain Science and Intelligence TechnologyCN27 papers
- Tsinghua UniversityCN18 papers
- Anhui UniversityCN7 papers
- Beijing Institute of TechnologyCN7 papers
- Peng Cheng LaboratoryCN7 papers
- Beijing University of Posts and TelecommunicationsCN6 papers
- National University of SingaporeSG6 papers
- Peking UniversityCN6 papers
18 papers · 1 filter
LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization
Boxiao Wang, Kai Li, Tianyi Liu +4
Symbolic regression aims to distill mathematical equations from observational data. Recent approaches have successfully leveraged Large Language Models (LLMs) to generate equation…
ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery
Shijie Ma, Fei Zhu, Xu-Yao Zhang +1
Generalized category discovery (GCD) is a pragmatic but underexplored problem, which requires models to automatically cluster and discover novel categories by leveraging the labele…
Fairness without Demographics through Learning Graph of Gradients
Yingtao Luo, Zhixun Li, Qiang Liu +1
Machine learning systems are notoriously prone to biased predictions about certain demographic groups, leading to algorithmic fairness issues. Due to privacy concerns and data qual…
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters
Guoming Li, Jian Yang, Shangsong Liang
Approximation-based spectral graph neural networks, which construct graph filters with function approximation, have shown substantial performance in graph learning tasks. Despite t…
Bi-Level Graph Structure Learning for Next POI Recommendation
Liang Wang, Shu Wu, Qiang Liu +3
Next point-of-interest (POI) recommendation aims to predict a user's next destination based on sequential check-in history and a set of POI candidates. Graph neural networks (GNNs)…
MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization
Zelin Zang, Yuhao Wang, Jinlin Wu +4
Dimensionality reduction (DR) plays a crucial role in various fields, including data engineering and visualization, by simplifying complex datasets while retaining essential inform…