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..
On Calibrating Diffusion Probabilistic Models
Authors: Tianyu Pang, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, Zhijie Deng
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on multiple datasets to empirically validate our proposal. |
| Researcher Affiliation | Collaboration | 1Sea AI Lab, Singapore 2Department of Computer Science, Tsinghua University 3Qing Yuan Research Institute, Shanghai Jiao Tong University |
| Pseudocode | No | No pseudocode or algorithm blocks are provided in the main text or appendices. |
| Open Source Code | Yes | Our code is available at https://github.com/thudzj/Calibrated-DPMs. |
| Open Datasets | Yes | We evaluate our calibration tricks on the CIFAR-10 [25] and Celeb A 64 64 [27] datasets |
| Dataset Splits | No | The paper refers to using "training data" but does not provide specific train/validation/test splits (percentages, counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments (e.g., GPU/CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper mentions software like PyTorch and Tensorflow checkpoints, and the Adam optimizer, but does not specify their version numbers or other software dependencies with explicit versions. |
| Experiment Setup | Yes | In accordance with the recommendation, we set the end time of DPM-Solver to 10 3 when the number of sampling steps is less than 15, and to 10 4 otherwise. Additional details can be found in Lu et al. [29]. |