Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality

arXiv:2607.26428 · cs.LG, stat.ML · Submitted 2026-07-29 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-07-29

Updated: 2026-09-26

Comments: 19 pages, 9 figures, 6 tables. Preprint

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Model — 32× in this paper · explained in 10 episodes
• Spatial Point Processes — 19× in this paper
• Diffusion Models — 16× in this paper · explained in 9 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Generative Modeling — 8× in this paper · explained in 3 episodes
• Permutation Invariance — 6× in this paper · explained in 2 episodes
• Diffusion Process — 5× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Diffusion Processes — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Synthetic Data Generation — 2× in this paper · explained in 5 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Graph Neural Network (GNN) — 1× in this paper · explained in 5 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Latent Variables — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 4 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Papers — 1× in this paper · explained in 3 episodes
• Training Objective — 1× in this paper · explained in 3 episodes
• Wasserstein Distance — 1× in this paper · explained in 6 episodes

Sources

• Neural Spatio-Temporal Point Processes
• Unlocking Point Processes through Point Set Diffusion
• Score-Based Generative Modeling through Stochastic Differential Equations

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks