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.
Improved Sample Complexity Bounds for Diffusion Model Training
Authors: Shivam Gupta, Aditya Parulekar, Eric Price, Zhiyang Xun
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,475 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,928 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we focus on understanding the sample complexity of training such a model; how many samples are needed to learn an accurate diffusion model using a sufficiently expressive neural network? Prior work [BMR20] showed bounds polynomial in the dimension, desired Total Variation error, and Wasserstein error. We show an exponential improvement in the dependence on Wasserstein error and depth, along with improved dependencies on other relevant parameters. |
| Researcher Affiliation | Academia | Shivam Gupta UT Austin EMAIL Aditya Parulekar UT Austin EMAIL Eric Price UT Austin EMAIL Zhiyang Xun UT Austin EMAIL |
| Pseudocode | Yes | Algorithm 1 Empirical score estimation for s |
| Open Source Code | No | We do not have any experiments. We have a small simulation that is just a illustration of a lower bound, which is very small in scope. |
| Open Datasets | No | The paper refers to training with "m i.i.d. samples xi q0" from an abstract distribution q0, but does not specify any named public datasets or provide access information for specific data used in experiments. |
| Dataset Splits | No | The paper does not conduct empirical studies with specific datasets, therefore, it does not specify any training/test/validation dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers used for experiments. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not include details about an experimental setup, hyperparameters, or system-level training settings. |