Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity
Authors: Haoxuan Chen, Yinuo Ren, Lexing Ying, Grant Rotskoff
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As a theoretical paper, we do not include experiments. |
| Researcher Affiliation | Academia | Haoxuan Chen ICME Stanford University EMAIL; Yinuo Ren ICME Stanford University EMAIL; Lexing Ying Department of Mathematics and ICME Stanford University EMAIL; Grant M. Rotskoff Department of Chemistry and ICME Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1: PIADM-SDE; Algorithm 2: PIADM-ODE |
| Open Source Code | No | As a theoretical paper, we do not include experiments. |
| Open Datasets | No | As a theoretical paper, we do not include experiments. |
| Dataset Splits | No | As a theoretical paper, we do not include experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for its own research or experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |