Adversarial Training from Mean Field Perspective
Authors: Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate Thms 5.1, 5.4 and 5.7 via numerical experiments. |
| Researcher Affiliation | Academia | Soichiro Kumano The University of Tokyo kumano@cvm.t.u-tokyo.ac.jp Hiroshi Kera Chiba University kera@chiba-u.jp Toshihiko Yamasaki The University of Tokyo yamasaki@cvm.t.u-tokyo.ac.jp |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We used MNIST [26] and Fashion-MNIST [99] as the training dataset. |
| Dataset Splits | No | The paper mentions using MNIST and Fashion-MNIST datasets but does not explicitly provide training/test/validation dataset splits or refer to predefined splits with specific details for reproducibility. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA A100. |
| Software Dependencies | No | The paper mentions optimizers like Adam [51] but does not provide specific version numbers for software dependencies such as programming languages or libraries. |
| Experiment Setup | Yes | The vanilla Re LU networks were initialized with σ2 w = 2 and σ2 b = 0.01 to meet the (M, m)-trainability conditions (Lemma 5.6). ... We used stochastic gradient descent with a small learning rate, 0.001. ... We set an optimizer to Adam, a learning rate to 0.001, and epochs to 200. |