:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lai, Jasen, Liang, Senwei, Wang, Chunmei
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2506.20607
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Epidemiological Dynamics via the Finite Expression Method
by: Du, Jianda, et al.
Published: (2024)

Identifying Unknown Stochastic Dynamics via Finite expression methods
by: Liang, Senwei, et al.
Published: (2025)

Finite Expression Method for Solving High-Dimensional Partial Differential Equations
by: Liang, Senwei, et al.
Published: (2022)

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
by: Hardwick, Gareth, et al.
Published: (2024)

Solving PDEs on Unknown Manifolds with Machine Learning
by: Liang, Senwei, et al.
Published: (2021)

Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation
by: Xie, Senwei, et al.
Published: (2026)

A Structure-Preserving Kernel Method for Learning Hamiltonian Systems
by: Hu, Jianyu, et al.
Published: (2024)

Finite Expression Methods for Discovering Physical Laws from Data
by: Jiang, Zhongyi, et al.
Published: (2023)

Data-efficient Kernel Methods for Learning Hamiltonian Systems
by: Jalalian, Yasamin, et al.
Published: (2025)

The finite expression method for turbulent dynamics with high-order moment recovery
by: Xu, Xingjian, et al.
Published: (2026)

Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks
by: Shmuel, Assaf, et al.
Published: (2023)

Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
by: Lai, Jinlin, et al.
Published: (2024)

Moment Estimates and DeepRitz Methods on Learning Diffusion Systems with Non-gradient Drifts
by: Kong, Fanze, et al.
Published: (2025)

A Mathematical Framework and a Suite of Learning Techniques for Neural-Symbolic Systems
by: Dickens, Charles, et al.
Published: (2024)

Learning Trajectories of Hamiltonian Systems with Neural Networks
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)

Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data
by: Thapar, Vedanta, et al.
Published: (2026)

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
by: Liang, Jiajun, et al.
Published: (2024)

A Pontryagin Method of Model-based Reinforcement Learning via Hamiltonian Actor-Critic
by: Gu, Chengyang, et al.
Published: (2026)

Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network
by: Chen, Chen, et al.
Published: (2025)

Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement
by: Liang, Haodong, et al.
Published: (2026)

An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis
by: Xie, Yi, et al.
Published: (2024)

Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
by: Daniele, Alessandro, et al.
Published: (2022)

A Vector Symbolic Approach to Multiple Instance Learning
by: Dhrubo, Ehsan Ahmed, et al.
Published: (2025)

Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
by: Keller, Leon, et al.
Published: (2025)

On the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)
by: Eslami, Mostafa, et al.
Published: (2025)

Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems
by: Nguyen, Binh, et al.
Published: (2026)

NPSVC++: Nonparallel Classifiers Encounter Representation Learning
by: Zhang, Junhong, et al.
Published: (2024)

A Learnability Analysis on Neuro-Symbolic Learning
by: He, Hao-Yuan, et al.
Published: (2025)

From Equations to Insights: Unraveling Symbolic Structures in PDEs with LLMs
by: Bhatnagar, Rohan, et al.
Published: (2025)

Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling
by: Liang, Xiaoxiao, et al.
Published: (2026)

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
by: Friedl, Katharina, et al.
Published: (2025)

Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
by: Hayes, Conor F., et al.
Published: (2025)

DEM-NeRF: A Neuro-Symbolic Method for Scientific Discovery through Physics-Informed Simulation
by: Tan, Wenkai, et al.
Published: (2025)

Softened Symbol Grounding for Neuro-symbolic Systems
by: Li, Zenan, et al.
Published: (2024)

Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
by: van Otterdijk, G. J. E., et al.
Published: (2024)

Prior-Guided Symbolic Regression: Towards Scientific Consistency in Equation Discovery
by: Xiao, Jing, et al.
Published: (2026)

Learning Hamiltonian Density Using DeepONet
by: Xu, Baige, et al.
Published: (2025)

Machine Learning-Based Graph Simplification for Symbolic Accelerators
by: Yu, Tiffany, et al.
Published: (2026)

Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network
by: Dave, Neisarg, et al.
Published: (2024)

Reinforcement Learning with Symbolic Reward Machines
by: Krug, Thomas, et al.
Published: (2026)