Cover Image

Neural Point Process for Learning Spatiotemporal Event Dynamics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhou, Zihao, Yang, Xingyi, Rossi, Ryan, Zhao, Handong, Yu, Rose
Format:	Preprint
Published:	2021
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes
by: Zhou, Wang-Tao, et al.
Published: (2025)

Neural Spatiotemporal Point Processes: Trends and Challenges
by: Mukherjee, Sumantrak, et al.
Published: (2025)

Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
by: Wu, Keshu, et al.
Published: (2026)

Better Generative Replay for Continual Federated Learning
by: Qi, Daiqing, et al.
Published: (2023)

On the Spatiotemporal Dynamics of Generalization in Neural Networks
by: Wei, Zichao
Published: (2026)

Implicit Neural Differential Model for Spatiotemporal Dynamics
by: Akhare, Deepak, et al.
Published: (2025)

Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
by: Minartz, Koen, et al.
Published: (2023)

Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks
by: Chakraborty, Biswadeep, et al.
Published: (2025)

Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
by: Kuang, Yiling, et al.
Published: (2024)

Federated Neural Nonparametric Point Processes
by: Chen, Hui, et al.
Published: (2024)

Symmetry in Neural Network Parameter Spaces
by: Zhao, Bo, et al.
Published: (2025)

Event-Aware Prompt Learning for Dynamic Graphs
by: Yu, Xingtong, et al.
Published: (2025)

DyMRL: Dynamic Multispace Representation Learning for Multimodal Event Forecasting in Knowledge Graph
by: Zhao, Feng, et al.
Published: (2026)

Neural Metamorphosis
by: Yang, Xingyi, et al.
Published: (2024)

Conformal Prediction for Time-series Forecasting with Change Points
by: Sun, Sophia, et al.
Published: (2025)

Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction
by: Mi, Yuan, et al.
Published: (2024)

Deep Graph Neural Point Process For Learning Temporal Interactive Networks
by: Chen, Su, et al.
Published: (2025)

Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
by: Dong, Xiangyu, et al.
Published: (2023)

Pruning for Generalization: A Transfer-Oriented Spatiotemporal Graph Framework
by: Jing, Zihao, et al.
Published: (2026)

SRNN: Spatiotemporal Relational Neural Network for Intuitive Physics Understanding
by: Yang, Fei
Published: (2025)

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

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

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields
by: Park, David Keetae, et al.
Published: (2025)

Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population
by: Chen, Suiyao, et al.
Published: (2024)

DCL-SE: Dynamic Curriculum Learning for Spatiotemporal Encoding of Brain Imaging
by: Zhou, Meihua, et al.
Published: (2025)

Prediction of Clinical Complication Onset using Neural Point Processes
by: Weerasekara, Sachini, et al.
Published: (2025)

Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
by: Wang, Yu, et al.
Published: (2023)

A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
by: Dong, Yiwei, et al.
Published: (2024)

From Observations to Events: Event-Aware World Model for Reinforcement Learning
by: Peng, Zhao-Han, et al.
Published: (2026)

Over-squashing in Spatiotemporal Graph Neural Networks
by: Marisca, Ivan, et al.
Published: (2025)

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
by: Yu, Dahai, et al.
Published: (2025)

Rényi Neural Processes
by: Wang, Xuesong, et al.
Published: (2024)

FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing
by: Barman, Pronob Kumar, et al.
Published: (2026)

G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
by: Beerman, Jack T., et al.
Published: (2026)

Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
by: Zheng, Yu, et al.
Published: (2024)

Neural Bridge Processes
by: Xu, Jian, et al.
Published: (2025)

Cumulative Distribution Function based General Temporal Point Processes
by: Wang, Maolin, et al.
Published: (2024)

Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
by: Zheng, Zinan, et al.
Published: (2024)

PIMRL: Physics-Informed Multi-Scale Recurrent Learning for Burst-Sampled Spatiotemporal Dynamics
by: Wan, Han, et al.
Published: (2025)

Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
by: Wu, Chenghan, et al.
Published: (2026)