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 [1].
A Flexible Diffusion Model
Authors: Weitao Du, He Zhang, Tao Yang, Yuanqi Du
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments on synthetic datasets, MNIST and CIFAR10 to validate the effectiveness of our framework. |
| Researcher Affiliation | Academia | 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University 3Cornell University. |
| Pseudocode | No | The paper describes algorithmic steps in prose and equations but does not present any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or include a link to a code repository. |
| Open Datasets | Yes | In this section, we demonstrate the generative capacity of our FP-Diffusion models on two common image datasets: MNIST (Le Cun, 1998) and CIFAR10 (Krizhevsky et al., 2009). |
| Dataset Splits | No | The paper describes a 'two-stage training strategy' but does not explicitly provide percentages, sample counts, or citations for specific training/validation/test dataset splits. |
| Hardware Specification | Yes | All the experiments are conducted on 4 Nvidia Tesla V100 16G GPUs. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify version numbers for any software components, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | For all experiments, we set ฮฒmax as 20 and ฮฒmin as 0.1... All models are trained with the Adam optimizer with a learning rate 2 ร 10โ4 and a batch size 96. In the MNIST experiment, we first train the whole model for 50k iterations and train the score model for another 250k iterations... In the CIFAR10 experiment, the training iterations of both stage 1 and stage 2 are 600k. |