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..
Formulating Discrete Probability Flow Through Optimal Transport
Authors: Pengze Zhang, Hubery Yin, Chen Li, Xiaohua Xie
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the synthetic toy dataset and the CIFAR-10 dataset have validated the effectiveness of our proposed discrete probability flow. |
| Researcher Affiliation | Collaboration | Pengze Zhang Sun Yat-sen University EMAIL Hubery Yin We Chat, Tencent Inc. EMAIL Chen Li We Chat, Tencent Inc. EMAIL Xiaohua Xie Sun Yat-sen University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks, nor clearly labeled algorithm sections or code-like formatted procedures. |
| Open Source Code | Yes | Code is released at: https://github.com/Pangze Cheung/Discrete-Probability-Flow. |
| Open Datasets | Yes | Extensive experiments on the synthetic toy dataset and the CIFAR-10 dataset have validated the effectiveness of our proposed discrete probability flow. |
| Dataset Splits | No | The paper discusses sample generation and initial points but does not provide specific dataset split information (exact percentages, sample counts for train/validation/test, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Experiments are conducted on synthetic data using the same setup as SDDM [47], with the exception that we replaced the generator Q with Equation (26). ... Specifically, we set s = 0 and t = T, and sample 4,000 xts with 10 xss for each xt. ... We use the Euler s method to generate samples. Given the time step length ϵ, the transition probabilities for dimension l is: |