Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
Authors: Kaiwen Xue, Yuhao Zhou, Shen Nie, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 31,281 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,198 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, using the same pre-trained model, our best solver significantly outperforms the original BFN sampler with a few (e.g., 10) number of function evaluations (NFE) under sample quality on both the CIFAR10 and text8 datasets, achieving a 5 20 times increase in speed for free (see Sec. 6 for details). |
| Researcher Affiliation | Collaboration | 1Gaoling School of AI, Renmin University of China, Beijing, China 2Department of Computer Science and Technology, Tsinghua University, Beijing, China 3Ant Group, Hangzhou, China. |
| Pseudocode | Yes | Algorithm 1 BFN-Solver++1 (on continuous data) ... Algorithm 2 BFN-Solver++2 (on continuous data) ... Algorithm 3 SDE-BFN-Solver++2 (on continuous data) ... Algorithm 4 SDE-BFN-Solver1 (on discrete data) ... Algorithm 5 SDE-BFN-Solver2 (on discrete data) ... Algorithm 6 BFN-Solver1 (on discrete data) ... Algorithm 7 BFN-Solver2 (on discrete data) |
| Open Source Code | Yes | Our code is available at https: //github.com/ML-GSAI/BFN-Solver. |
| Open Datasets | Yes | For continuous data, the model is trained on the CIFAR-10 (Krizhevsky & Hinton, 2009) dataset which contain 50K training images. For discrete data, the model is trained on the text8 (Mahoney, 2011) dataset which contains 90M consecutive characters, each character is a lower Latin letter a z or the whitespace token, giving a class number of 27. |
| Dataset Splits | No | The paper mentions training on CIFAR-10 and text8 datasets but does not explicitly provide details about training/validation/test splits (e.g., percentages, counts, or references to standard splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'pre-trained models provided by the BFN (Graves et al., 2023)' but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We slightly tune the hyperparameter η for our methods on different NFEs to get the best results, as detailed in Appendix D.3. |