Modern Structure-Aware Simplicial Spatiotemporal Neural Network
Fuente:
arXiv
Guardado en:
| Autores principales: | Hu, Zhaobo, Gauthier, Vincent, Naima, Mehdi |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Beyond the Laplacian: Doubly Stochastic Matrices for Graph Neural Networks
por: Hu, Zhaobo, et al.
Publicado: (2026)
por: Hu, Zhaobo, et al.
Publicado: (2026)
Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift
por: Hu, Zhaobo, et al.
Publicado: (2026)
por: Hu, Zhaobo, et al.
Publicado: (2026)
Continuous Simplicial Neural Networks
por: Einizade, Aref, et al.
Publicado: (2025)
por: Einizade, Aref, et al.
Publicado: (2025)
Binarized Simplicial Convolutional Neural Networks
por: Yan, Yi, et al.
Publicado: (2024)
por: Yan, Yi, et al.
Publicado: (2024)
Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation
por: Jing, Baoyu, et al.
Publicado: (2024)
por: Jing, Baoyu, et al.
Publicado: (2024)
Generalized Simplicial Attention Neural Networks
por: Battiloro, Claudio, et al.
Publicado: (2023)
por: Battiloro, Claudio, et al.
Publicado: (2023)
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
por: Lecha, Manuel, et al.
Publicado: (2024)
por: Lecha, Manuel, et al.
Publicado: (2024)
Trainable and Explainable Simplicial Map Neural Networks
por: Paluzo-Hidalgo, Eduardo, et al.
Publicado: (2023)
por: Paluzo-Hidalgo, Eduardo, et al.
Publicado: (2023)
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
por: Beerman, Jack T., et al.
Publicado: (2026)
por: Beerman, Jack T., et al.
Publicado: (2026)
Structure and Scale in Simplicial Sequence Modelling
por: Farrugia-Roberts, Matthew
Publicado: (2026)
por: Farrugia-Roberts, Matthew
Publicado: (2026)
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
por: Zhuang, Dingyi, et al.
Publicado: (2024)
por: Zhuang, Dingyi, et al.
Publicado: (2024)
Spatiotemporal Covariance Neural Networks
por: Cavallo, Andrea, et al.
Publicado: (2024)
por: Cavallo, Andrea, et al.
Publicado: (2024)
Geometry-Aware Simplicial Message Passing
por: Wang, Elena Xinyi, et al.
Publicado: (2026)
por: Wang, Elena Xinyi, et al.
Publicado: (2026)
Simplicial Representation Learning with Neural $k$-Forms
por: Maggs, Kelly, et al.
Publicado: (2023)
por: Maggs, Kelly, et al.
Publicado: (2023)
On the Spatiotemporal Dynamics of Generalization in Neural Networks
por: Wei, Zichao
Publicado: (2026)
por: Wei, Zichao
Publicado: (2026)
Topological Trajectory Classification and Landmark Inference on Simplicial Complexes
por: Grande, Vincent P., et al.
Publicado: (2024)
por: Grande, Vincent P., et al.
Publicado: (2024)
Performative Prediction with Neural Networks
por: Mofakhami, Mehrnaz, et al.
Publicado: (2023)
por: Mofakhami, Mehrnaz, et al.
Publicado: (2023)
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
por: Ferbach, Damien, et al.
Publicado: (2023)
por: Ferbach, Damien, et al.
Publicado: (2023)
Over-squashing in Spatiotemporal Graph Neural Networks
por: Marisca, Ivan, et al.
Publicado: (2025)
por: Marisca, Ivan, et al.
Publicado: (2025)
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
por: Zhuang, Dingyi, et al.
Publicado: (2025)
por: Zhuang, Dingyi, et al.
Publicado: (2025)
Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery
por: Guenter, Valentin Frank Ingmar, et al.
Publicado: (2024)
por: Guenter, Valentin Frank Ingmar, et al.
Publicado: (2024)
Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks
por: Einizade, Aref, et al.
Publicado: (2024)
por: Einizade, Aref, et al.
Publicado: (2024)
Leveraging Spatiotemporal Graph Neural Networks for Multi-Store Sales Forecasting
por: Singh, Manish, et al.
Publicado: (2025)
por: Singh, Manish, et al.
Publicado: (2025)
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
por: Eschenhagen, Runa, et al.
Publicado: (2023)
por: Eschenhagen, Runa, et al.
Publicado: (2023)
AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
por: Zhang, Zhongyue, et al.
Publicado: (2026)
por: Zhang, Zhongyue, et al.
Publicado: (2026)
Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks
por: Xue, Zhihao, et al.
Publicado: (2025)
por: Xue, Zhihao, et al.
Publicado: (2025)
Pruning and Quantization Impact on Graph Neural Networks
por: Khedri, Khatoon, et al.
Publicado: (2025)
por: Khedri, Khatoon, et al.
Publicado: (2025)
A Unification of Discrete, Gaussian, and Simplicial Diffusion
por: Chandra, Nuria Alina, et al.
Publicado: (2025)
por: Chandra, Nuria Alina, et al.
Publicado: (2025)
Graph Neural Networks with Feature and Structure Aware Random Walk
por: Zhuo, Wei, et al.
Publicado: (2021)
por: Zhuo, Wei, et al.
Publicado: (2021)
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
por: Wang, Qingyi, et al.
Publicado: (2023)
por: Wang, Qingyi, et al.
Publicado: (2023)
SRNN: Spatiotemporal Relational Neural Network for Intuitive Physics Understanding
por: Yang, Fei
Publicado: (2025)
por: Yang, Fei
Publicado: (2025)
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
por: Wang, Yu, et al.
Publicado: (2023)
por: Wang, Yu, et al.
Publicado: (2023)
SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
por: Luo, Xinyu, et al.
Publicado: (2025)
por: Luo, Xinyu, et al.
Publicado: (2025)
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering
por: Fayyazi, Arya, et al.
Publicado: (2025)
por: Fayyazi, Arya, et al.
Publicado: (2025)
PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling
por: Nguyen, Phong C. H., et al.
Publicado: (2024)
por: Nguyen, Phong C. H., et al.
Publicado: (2024)
GeoClip: Geometry-Aware Clipping for Differentially Private SGD
por: Gilani, Atefeh, et al.
Publicado: (2025)
por: Gilani, Atefeh, et al.
Publicado: (2025)
Graph Neural Networks in Modern AI-aided Drug Discovery
por: Zhang, Odin, et al.
Publicado: (2025)
por: Zhang, Odin, et al.
Publicado: (2025)
Amortising Inference and Meta-Learning Priors in Neural Networks
por: Rochussen, Tommy, et al.
Publicado: (2026)
por: Rochussen, Tommy, et al.
Publicado: (2026)
Towards Scalable and Structured Spatiotemporal Forecasting
por: Chen, Hongyi, et al.
Publicado: (2025)
por: Chen, Hongyi, et al.
Publicado: (2025)
Dynamic Modes as Time Representation for Spatiotemporal Forecasting
por: Kong, Menglin, et al.
Publicado: (2025)
por: Kong, Menglin, et al.
Publicado: (2025)
Ejemplares similares
-
Beyond the Laplacian: Doubly Stochastic Matrices for Graph Neural Networks
por: Hu, Zhaobo, et al.
Publicado: (2026) -
Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift
por: Hu, Zhaobo, et al.
Publicado: (2026) -
Continuous Simplicial Neural Networks
por: Einizade, Aref, et al.
Publicado: (2025) -
Binarized Simplicial Convolutional Neural Networks
por: Yan, Yi, et al.
Publicado: (2024) -
Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation
por: Jing, Baoyu, et al.
Publicado: (2024)