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..
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points
Authors: Albert No, Taeho Yoon, Kwon Sehyun, Ernest K Ryu
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 6 presents an experiment with a mixture of 8 Gaussians and Ng = 5, 000. The experiments demonstrate the sufficiency of two-layer networks with random features and that the training does not encounter local minima when Ng is large. Figure 5 visualizes the loss landscape with generator widths Ng = 2 and Ng = 10. For the Ng = 10 case, the parameter space was projected down to a 2D space defined by random directions, as recommended by Li et al. (2018b). We observe the landscape becomes more favorable with larger width. |
| Researcher Affiliation | Academia | 1Department of Electronic and Electrical Engineering, Hongik University, Seoul, Korea 2Department of Mathematical Sciences, Seoul National University, Seoul, Korea. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/sehyunkwon/Infinite-WGAN. |
| Open Datasets | No | Figure 6 presents an experiment with a mixture of 8 Gaussians and Ng = 5, 000. The paper does not provide concrete access information for this dataset. |
| Dataset Splits | No | The paper describes experiments with synthetic data (mixture of Gaussians) but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We fix the maximization stepsize to 1 while letting the minimization stepsize be α > 0. ... Ng = 5, 000, and Nd = 1, 000. ... with generator widths Ng = 2 and Ng = 10. |