:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wang, Chu, Wu, Jinhong, Wang, Yanzhi, Zha, Zhijian, Zhou, Qi
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2403.02810
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MPIPN: A Multi Physics-Informed PointNet for solving parametric acoustic-structure systems
by: Wang, Chu, et al.
Published: (2024)

Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis
by: Wang, Yanzhi, et al.
Published: (2024)

DyJR: Preserving Diversity in Reinforcement Learning with Verifiable Rewards via Dynamic Jensen-Shannon Replay
by: Li, Long, et al.
Published: (2026)

Binary structured physics-informed neural networks for solving equations with rapidly changing solutions
by: Liu, Yanzhi, et al.
Published: (2024)

Imagination-Limited Q-Learning for Offline Reinforcement Learning
by: Liu, Wenhui, et al.
Published: (2025)

Estimating unknown parameters in differential equations with a reinforcement learning based PSO method
by: Sun, Wenkui, et al.
Published: (2024)

Invertible Koopman neural operator for data-driven modeling of partial differential equations
by: Jin, Yuhong, et al.
Published: (2025)

Prompt-Driven Continual Graph Learning
by: Wang, Qi, et al.
Published: (2025)

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
by: Chu, Xu, et al.
Published: (2025)

Personalized Federated Learning via Learning Dynamic Graphs
by: Zhou, Ziran, et al.
Published: (2025)

Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
by: Della Santa, Francesco, et al.
Published: (2026)

Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning
by: Wang, Ling, et al.
Published: (2024)

Discretization-independent multifidelity operator learning for partial differential equations
by: Hauck, Jacob, et al.
Published: (2025)

Physics-informed fine-tuning of foundation models for partial differential equations
by: Medvedev, Vlad, et al.
Published: (2026)

The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
by: Li, Long, et al.
Published: (2025)

Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
by: Kumar, Sawan, et al.
Published: (2024)

Tensor Graph Convolutional Network for Dynamic Graph Representation Learning
by: Wang, Ling, et al.
Published: (2024)

Neural delay differential equations: learning non-Markovian closures for partially known dynamical systems
by: Monsel, Thibault, et al.
Published: (2024)

Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
by: Li, Zijie, et al.
Published: (2024)

Breakeven complexity: A new perspective on neural partial differential equation solvers
by: Zhang, Yijing, et al.
Published: (2026)

Fast and Robust Contextual Node Representation Learning over Dynamic Graphs
by: Guo, Xingzhi, et al.
Published: (2024)

An Operator-Consistent Graph Neural Network for Learning Diffusion Dynamics on Irregular Meshes
by: Li, Yuelian, et al.
Published: (2025)

Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models
by: Zhuo, Zhijian, et al.
Published: (2024)

GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models
by: Fang, Yi, et al.
Published: (2024)

Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness
by: Tan, Xuwei, et al.
Published: (2021)

Directed Acyclic Graph Structure Learning from Dynamic Graphs
by: Fan, Shaohua, et al.
Published: (2022)

LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs
by: Hao, Bing, et al.
Published: (2025)

Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning
by: Wang, Xiaoyi, et al.
Published: (2026)

Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
by: Zhou, Zhijian, et al.
Published: (2025)

Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
by: Zhang, Zeyang, et al.
Published: (2024)

EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework
by: Wang, Chen, et al.
Published: (2025)

Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning
by: Wu, Rixin, et al.
Published: (2023)

CLDG: Contrastive Learning on Dynamic Graphs
by: Xu, Yiming, et al.
Published: (2024)

E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification
by: Zhang, Xin, et al.
Published: (2024)

From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
by: Li, Zhihao, et al.
Published: (2026)

GraphBridge: Towards Arbitrary Transfer Learning in GNNs
by: Ju, Li, et al.
Published: (2025)

Graph Propagation Transformer for Graph Representation Learning
by: Chen, Zhe, et al.
Published: (2023)

Characterizing the Influence of Topology on Graph Learning Tasks
by: Wu, Kailong, et al.
Published: (2024)

Efficient Reasoning with Hidden Thinking
by: Shen, Xuan, et al.
Published: (2025)

Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping
by: Zhan, Simon Sinong, et al.
Published: (2024)