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..
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity
Authors: Chengyue Gong, Lemeng Wu, Qiang Liu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our method can efficiently generate diverse solutions on multiple applications, e.g. text-to-image generation, text-to-mesh generation, molecular conformation generation and ensemble neural network training. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Texas at Austin. |
| Pseudocode | Yes | Algorithm 1 Diversity-aware Gradient Descent (Fsum) |
| Open Source Code | No | The paper links to 'https://github.com/NVlabs/stylegan2-ada-pytorch' in the appendix, which is a third-party tool used in their experiments, not the open-source code for their proposed methodology. There is no explicit statement or link indicating the release of their own source code. |
| Open Datasets | Yes | We use Big GAN for Image Net image generation and Style GAN-v2 for high-resolution image generation. |
| Dataset Splits | No | The paper mentions training and testing on datasets but does not explicitly provide details on how the datasets were split into training, validation, and test sets, nor does it mention a specific validation set split. |
| Hardware Specification | Yes | Hours is measured on a NVIDIA Ge Force RTX3090 GPU. |
| Software Dependencies | No | The paper mentions software components like 'Adam (Kingma & Ba, 2014) optimizer', 'Big GAN', 'Style GAN-v2', and 'Res Net-56 models', but does not provide specific version numbers for any programming languages, libraries, or frameworks used. |
| Experiment Setup | Yes | We adopt gradient descent with a constant learning rate 5 10 4 and 1,000 iterations. |