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..
Sinkhorn Natural Gradient for Generative Models
Authors: Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we quantitatively compare Si NG with state-of-the-art SGD-type solvers on generative tasks to demonstrate its efficiency and efficacy of our method. |
| Researcher Affiliation | Academia | Department of Electrical and Systems Engineering :Department of Mathematics University of Pennsylvania {zebang@seas,zwang423@math,aribeiro@seas,hassani@seas}.upenn.edu |
| Pseudocode | No | The paper describes algorithms conceptually and refers to their implementation, but it does not include any explicitly labeled pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper refers to an Appendix E for implementation details but does not provide an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | In our experiments, we pretrain the discriminators for the celeb A and cifar10 datasets. [...] Our experiment considers a specific instance of problem (30) where we take the measure β to be the distribution of the images in the Celeb A dataset. [...] Two specific instances are considered: we take the measure β to be the distribution of the images in either the Celeb A or the Cifar10 dataset. |
| Dataset Splits | No | The paper mentions using Celeb A and Cifar10 datasets but does not specify the training, validation, or test splits (e.g., percentages, sample counts, or references to standard splits with citations) within the provided text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'modern auto-differential mechanisms such as Py Torch' and 'ADAM as the optimizer' but does not specify version numbers for any software dependencies, which are necessary for reproducibility. |
| Experiment Setup | Yes | The entropy regularization parameter γ is set to 0.01 for both the objective and the constraint. [...] The entropy regularization of the Sinkhorn divergence objective is set to γ 100 as suggested in Table 2 of [Genevay et al., 2018]. The regularization for the constraint is set to γ 1 in Si NG. We used ADAM as the optimizer for the discriminators (with step size 10 3 and batch size 4000). |