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..
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
Authors: Xunpeng Huang, Difan Zou, Hanze Dong, Zhang, Yian Ma, Tong Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments support our theory. In this section, we conduct experiments when the target distribution p is a Mixture of Gaussian (Mo G) and compare RTK-based methods with traditional DDPM. |
| Researcher Affiliation | Collaboration | Xunpeng Huang HKUST EMAIL Difan Zou HKU EMAIL Hanze Dong Salesforce AI Research EMAIL Yi Zhang HKU EMAIL Yian Ma UC San Diego EMAIL Tong Zhang UIUC EMAIL |
| Pseudocode | Yes | Algorithm 1 INFERENCE WITH REVERSE TRANSITION KERNEL (RTK) Algorithm 2 MALA/PROJECTED MALA FOR RTK INFERENCE Algorithm 3 ULD FOR RTK INFERENCE |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | In this section, we conduct experiments when the target distribution p is a Mixture of Gaussian (Mo G) and compare RTK-based methods with traditional DDPM. Furthermore, we conducted experiments on the MNIST dataset, as shown in Figure 6. |
| Dataset Splits | No | The paper mentions training models but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | The experiments are taken on a single NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Specifically, while DDPM models xη across a sequence of η timesteps spanning from 0 to T in increments of 0.001 T (i.e., [0, 0.001T, 0.002T, . . . , T]), we execute Alg. 1, 2, and 3 at fewer timesteps within x[0,0.2T,0.4T,0.6T,0.8T ], and we distribute the NFE uniformly to these timesteps for MCMC. |