
## Wide Two-Layer Networks can Learn from Adversarial Perturbations
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
Keywords: 
NeurIPS/2024/Proceedings/96861 - Wide Two-Layer Networks can Learn from Adversarial Perturbations.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[2]

Starting with 1 since there is code available. No extra cost since there are clear instructions for setup and running the code. 1 additional cost for very few comments in the code. Repository structure is easy to navigate, no extra cost.


### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[1]

(1/1)

The authors provide links with ``All generated data, including synthetic datasets, model parameters, and adversarial examples''

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[2]

The authors state configurations in chapter 4 and provide links with ``All generated data, including synthetic datasets, model parameters, and adversarial examples''. Looking at the appendix and implementation, they are well summarised and set the values but they do not state how.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The paper is theoretical with some experiments to support the theoretical findings. Thus, the experimental setup is only described briefly in the paper. However, Appendix B details many experimental settings. 

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[5]

The paper is theoretical and contains a lot of mathematics. However, it is generally well introduced and the authors use conventional notation, which eases understanding.
The paper touches upon adversarial machine learning only.
Since theoretical paper require more expertise, I end up grading fairly high with 5 despite the extensive information.