Approximation and Convergence Properties of Generative Adversarial Learning
Authors: Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 2To the best of our knowledge, neither (3) or (4) was used in any GAN algorithm. However, since our focus in this paper is not implementing new algorithms, we leave experiments with this formulation for future work. |
| Researcher Affiliation | Collaboration | Shuang Liu University of California, San Diego shuangliu@ucsd.edu Olivier Bousquet Google Brain obousquet@google.com Kamalika Chaudhuri University of California, San Diego kamalika@cs.ucsd.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | 2To the best of our knowledge, neither (3) or (4) was used in any GAN algorithm. However, since our focus in this paper is not implementing new algorithms, we leave experiments with this formulation for future work. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments, therefore no datasets are used or made publicly available for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no specific dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, therefore no specific hardware details are provided. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, therefore no specific experimental setup details or hyperparameters are provided. |