Formulating Discrete Probability Flow Through Optimal Transport
Authors: Pengze Zhang, Hubery Yin, Chen Li, Xiaohua Xie
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 zhangpz3@mail2.edu.cn Hubery Yin We Chat, Tencent Inc. hubery@tencent.com Chen Li We Chat, Tencent Inc. chaselli@tencent.com Xiaohua Xie Sun Yat-sen University xiexiaoh6@mail.edu.cn |
| 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: |