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..
Adversarial Training from Mean Field Perspective
Authors: Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
NeurIPS 2023 | Venue PDF | 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 EMAIL Hiroshi Kera Chiba University EMAIL Toshihiko Yamasaki The University of Tokyo EMAIL |
| 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. |