Saved in:
| Main Author: | Yang, Fei |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.06761 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN
by: Mohammadshirazi, Ahmad, et al.
Published: (2024)
by: Mohammadshirazi, Ahmad, et al.
Published: (2024)
On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight
by: Mustapha, Ismail B., et al.
Published: (2026)
by: Mustapha, Ismail B., et al.
Published: (2026)
On the Spatiotemporal Dynamics of Generalization in Neural Networks
by: Wei, Zichao
Published: (2026)
by: Wei, Zichao
Published: (2026)
Over-squashing in Spatiotemporal Graph Neural Networks
by: Marisca, Ivan, et al.
Published: (2025)
by: Marisca, Ivan, et al.
Published: (2025)
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
by: Wang, Yu, et al.
Published: (2023)
by: Wang, Yu, et al.
Published: (2023)
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
by: Beerman, Jack T., et al.
Published: (2026)
by: Beerman, Jack T., et al.
Published: (2026)
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
by: Yu, Dahai, et al.
Published: (2025)
by: Yu, Dahai, et al.
Published: (2025)
Neural Point Process for Learning Spatiotemporal Event Dynamics
by: Zhou, Zihao, et al.
Published: (2021)
by: Zhou, Zihao, et al.
Published: (2021)
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
by: Der, Audrey, et al.
Published: (2024)
by: Der, Audrey, et al.
Published: (2024)
Understanding Pooling in Graph Neural Networks
by: Grattarola, Daniele, et al.
Published: (2021)
by: Grattarola, Daniele, et al.
Published: (2021)
Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
by: Wu, Chenghan, et al.
Published: (2026)
by: Wu, Chenghan, et al.
Published: (2026)
The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer
by: Chen, Tianhua
Published: (2026)
by: Chen, Tianhua
Published: (2026)
Learning to Play Video Games with Intuitive Physics Priors
by: Jaiswal, Abhishek, et al.
Published: (2024)
by: Jaiswal, Abhishek, et al.
Published: (2024)
Unifying Adversarial Perturbation for Graph Neural Networks
by: Yang, Jinluan, et al.
Published: (2025)
by: Yang, Jinluan, et al.
Published: (2025)
Implicit Neural Differential Model for Spatiotemporal Dynamics
by: Akhare, Deepak, et al.
Published: (2025)
by: Akhare, Deepak, et al.
Published: (2025)
Neural Spatiotemporal Point Processes: Trends and Challenges
by: Mukherjee, Sumantrak, et al.
Published: (2025)
by: Mukherjee, Sumantrak, et al.
Published: (2025)
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
by: Minartz, Koen, et al.
Published: (2023)
by: Minartz, Koen, et al.
Published: (2023)
Systematic Relational Reasoning With Epistemic Graph Neural Networks
by: Khalid, Irtaza, et al.
Published: (2024)
by: Khalid, Irtaza, et al.
Published: (2024)
Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
by: Yang, Zhengxin, et al.
Published: (2024)
by: Yang, Zhengxin, et al.
Published: (2024)
Understanding Task Representations in Neural Networks via Bayesian Ablation
by: Nam, Andrew, et al.
Published: (2025)
by: Nam, Andrew, et al.
Published: (2025)
Understanding the Generalization of Stochastic Gradient Adam in Learning Neural Networks
by: Tang, Xuan, et al.
Published: (2025)
by: Tang, Xuan, et al.
Published: (2025)
A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning
by: Ji, Yixin, et al.
Published: (2025)
by: Ji, Yixin, et al.
Published: (2025)
A Novel Spatiotemporal Coupling Graph Convolutional Network
by: Bi, Fanghui
Published: (2024)
by: Bi, Fanghui
Published: (2024)
Relational Composition in Neural Networks: A Survey and Call to Action
by: Wattenberg, Martin, et al.
Published: (2024)
by: Wattenberg, Martin, et al.
Published: (2024)
A Relational Inductive Bias for Dimensional Abstraction in Neural Networks
by: Campbell, Declan, et al.
Published: (2024)
by: Campbell, Declan, et al.
Published: (2024)
Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
by: Qing, Yong, et al.
Published: (2023)
by: Qing, Yong, et al.
Published: (2023)
Spatiotemporal Graph Neural Networks in short term load forecasting: Does adding Graph Structure in Consumption Data Improve Predictions?
by: Nguyen, Quoc Viet, et al.
Published: (2025)
by: Nguyen, Quoc Viet, et al.
Published: (2025)
Complex Physics-Informed Neural Network
by: Si, Chenhao, et al.
Published: (2025)
by: Si, Chenhao, et al.
Published: (2025)
Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)
by: Raissi, Maziar, et al.
Published: (2024)
Scalable Spatiotemporal Prediction with Bayesian Neural Fields
by: Saad, Feras, et al.
Published: (2024)
by: Saad, Feras, et al.
Published: (2024)
Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
by: Wu, Keshu, et al.
Published: (2026)
by: Wu, Keshu, et al.
Published: (2026)
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction
by: Sattar, Asma, et al.
Published: (2024)
by: Sattar, Asma, et al.
Published: (2024)
Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
by: Li, Mengfan, et al.
Published: (2024)
by: Li, Mengfan, et al.
Published: (2024)
Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling
by: Zhang, Zhiguo, et al.
Published: (2025)
by: Zhang, Zhiguo, et al.
Published: (2025)
Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations
by: Cunegatti, Elia, et al.
Published: (2023)
by: Cunegatti, Elia, et al.
Published: (2023)
Understanding the Countably Infinite: Neural Network Models of the Successor Function and its Acquisition
by: Gupta, Vima, et al.
Published: (2023)
by: Gupta, Vima, et al.
Published: (2023)
Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration
by: Lagomarsino, Marta, et al.
Published: (2025)
by: Lagomarsino, Marta, et al.
Published: (2025)
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
by: Liu, Yang, et al.
Published: (2023)
by: Liu, Yang, et al.
Published: (2023)
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
by: Zheng, Zinan, et al.
Published: (2024)
by: Zheng, Zinan, et al.
Published: (2024)
Diagnosis of Fuel Cell Health Status with Deep Sparse Auto-Encoder Neural Network
by: Fei, Chenyan, et al.
Published: (2025)
by: Fei, Chenyan, et al.
Published: (2025)
Similar Items
-
PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN
by: Mohammadshirazi, Ahmad, et al.
Published: (2024) -
On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight
by: Mustapha, Ismail B., et al.
Published: (2026) -
On the Spatiotemporal Dynamics of Generalization in Neural Networks
by: Wei, Zichao
Published: (2026) -
Over-squashing in Spatiotemporal Graph Neural Networks
by: Marisca, Ivan, et al.
Published: (2025) -
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
by: Wang, Yu, et al.
Published: (2023)