14 citations · 20 across the 15 of their papers we have counts for
15 papers
Deep Divide-and-Reduce in Symbolic Regression
Yusong Deng, Yanjie Li, Weijun Li +6
Symbolic regression (SR) aims to discover underlying mathematical expressions from data while preserving interpretability. Most existing learning-based SR methods primarily optimiz…
CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials
Yanjie Li, Jian Xu, Xu-Yao Zhang +4
Property prediction and inverse structural design of catalytic materials are typically modeled as two independent tasks: the former predicts target properties from given structures…
GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing
Yanjie Li, Liping Zhang, Min Wu +6
Mathematical formulas serve as a language through which humans communicate with nature. Discovering mathematical laws from scientific data to describe natural phenomena has been a…
QE-Catalytic: A Graph-Language Multimodal Base Model for Relaxed-Energy Prediction in Catalytic Adsorption
Yanjie Li, Jian Xu, Xueqing Chen +4
Adsorption energy is a key descriptor of catalytic reactivity. It is fundamentally defined as the difference between the relaxed total energy of the adsorbate-surface system and th…
Operator Feature Neural Network for Symbolic Regression
Yusong Deng, Min Wu, Lina Yu +4
Symbolic regression is a task aimed at identifying patterns in data and representing them through mathematical expressions, generally involving skeleton prediction and constant opt…
DN-CL: Deep Symbolic Regression against Noise via Contrastive Learning
Jingyi Liu, Yanjie Li, Lina Yu +6
Noise ubiquitously exists in signals due to numerous factors including physical, electronic, and environmental effects. Traditional methods of symbolic regression, such as genetic…
ChatSR: Multimodal Large Language Models for Scientific Formula Discovery
Yanjie Li, Lina Yu, Weijun Li +6
Current multimodal large language models (MLLMs) are mainly focused on the understanding and processing of perceptual modalities such as images and videos, while their capability f…
Closed-form Solutions: A New Perspective on Solving Differential Equations
Shu Wei, Yanjie Li, Lina Yu +8
The quest for analytical solutions to differential equations has traditionally been constrained by the need for extensive mathematical expertise. Machine learning methods like gene…
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
Yanjie Li, Weijun Li, Lina Yu +6
The mathematical formula is the human language to describe nature and is the essence of scientific research. Finding mathematical formulas from observational data is a major demand…
MMSR: Symbolic Regression is a Multi-Modal Information Fusion Task
Yanjie Li, Jingyi Liu, Weijun Li +6
Mathematical formulas are the crystallization of human wisdom in exploring the laws of nature for thousands of years. Describing the complex laws of nature with a concise mathemati…
Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Search
Yanjie Li, Weijun Li, Lina Yu +6
Finding a concise and interpretable mathematical formula that accurately describes the relationship between each variable and the predicted value in the data is a crucial task in s…
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression
Min Wu, Weijun Li, Lina Yu +4
Symbolic regression aims to derive interpretable symbolic expressions from data in order to better understand and interpret data. %which plays an important role in knowledge discov…
A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure
Yanjie Li, Weijun Li, Lina Yu +14
Multilayer perception (MLP) has permeated various disciplinary domains, ranging from bioinformatics to financial analytics, where their application has become an indispensable face…
MetaSymNet: A Tree-like Symbol Network with Adaptive Architecture and Activation Functions
Yanjie Li, Weijun Li, Lina Yu +6
Mathematical formulas serve as the means of communication between humans and nature, encapsulating the operational laws governing natural phenomena. The concise formulation of thes…
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
Wenqiang Li, Weijun Li, Lina Yu +7
Symbolic regression (SR) is a powerful technique for discovering the underlying mathematical expressions from observed data. Inspired by the success of deep learning, recent deep g…