Outlier-Aware Post-Training Quantization for Discrete Graph Diffusion Models

Zheng Gong, Ying Sun
2/14/2026

No confident code implementation found for this paper. 3 possible matches can be shown below.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Shape-Guided Dual-Memory Learning for 3D Anomaly Detection

100% match1 repo502026

Constraint-Conditioned Actor-Critic for Offline Safe Reinforcement Learning

100% match1 repo82026

Zero-Shot Structure-Preserving Diffusion Model for High Dynamic Range Tone Mapping

100% match1 repo352026

Collaborative Discrete-Continuous Black-Box Prompt Learning for Language Models

100% match1 repo12026

Wavelength-Embedding-Guided Filter-Array Transformer for Spectral Demosaicing

99% match2026

Global-To-Pixel Regression for Human Mesh Recovery

99% match2026

Cite this paper

@article{gong2026outlieraware,
  title  = {Outlier-Aware Post-Training Quantization for Discrete Graph Diffusion Models},
  author = {Zheng Gong and Ying Sun},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:54b9e2239838db7383ea3a9f71b8ce13a8eb8414},
  journal = {ICML 2025 2025}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/54b9e2239838db7383ea3a9f71b8ce13a8eb8414.svg)](https://codeofpaper.com/paper/54b9e2239838db7383ea3a9f71b8ce13a8eb8414)

Discussion