Efficient Graph Generation via Direct Prediction and Flow Matching
cs.LG
Submitted: 2026-10-04
Updated: 2026-10-04
Code: https://github.com/Susie-Lu/DiGFM
Terminology
Sources
- Non-Denoising Forward-Time Diffusions
- Principled Latent Diffusion for Graphs via Laplacian Autoencoders
- Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
- Categorical Normalizing Flows via Continuous Transformations
- Cometh: A continuous-time discrete-state graph diffusion model
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