14 citations · 21 across the 12 of their papers we have counts for
12 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…
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…
Learning Continuous Face Representation with Explicit Functions
Liping Zhang, Weijun Li, Linjun Sun +5
How to represent a face pattern? While it is presented in a continuous way in our visual system, computers often store and process the face image in a discrete manner with 2D array…