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..
On Investigating the Conservative Property of Score-Based Generative Models
Authors: Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Chun-Yi Lee
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, our experimental results on the CIFAR10, CIFAR-100, Image Net, and SVHN datasets validate the effectiveness of QCSBMs. |
| Researcher Affiliation | Collaboration | 1Elsa Lab, Department of Computer Science, National Tsing Hua University, Taiwan. 2NVIDIA AI Technology Center, NVIDIA Corporation. |
| Pseudocode | Yes | Algorithm 1 Training Procedure of QCSBM |
| Open Source Code | Yes | The code implementation for the experiments is provided in the following repository: https://github.com/chen-hao-chao/qcsbm. |
| Open Datasets | Yes | CIFAR-10, CIFAR-100 (Krizhevsky & Hinton, 2009), Image Net-32x32 (Van Oord et al., 2016), and SVHN (Netzer et al., 2011) datasets. |
| Dataset Splits | No | The paper specifies 'training and test sets' with their sizes but does not mention explicit validation splits or percentages. |
| Hardware Specification | Yes | The results are evaluated on a single NVIDIA V100 GPU with 32 GB memory, and the batch size is fixed at 32. |
| Software Dependencies | No | The paper mentions 'Pytorch framework' and 'scipy.integrate.solve ivp library' but does not specify their version numbers. |
| Experiment Setup | Yes | The SBMs s U and s C are trained using the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 7.5 10 4 and a batch size of 5, 000. The balancing factor λ is fixed to 0.1. The maximal and minimal noise scales σmax and σmin are set to 3 and 0.1, respectively. |