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 [1].
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. |