## Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
Manuel Brenner, Florian Hess, Jonas M Mikhaeil, Leonard Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz
Keywords: 
ICML/2022/Proceedings/17351 - Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems.pdf
Project URL: nan

### 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?_

[4]

The code is directly linked inside the paper. However, the GitHub repository is poorly structured. In the main Readme, there is no guide about the code itself. In addition, there are more than one Readme in the subfolders of the repo, which are also poorly built. The file structure of the code is confusing and it is not intuitive.

### 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?_

[3]

(6/6)

The datasets are mentioned in the text with citations. However, there are not explained, statistics not provided.

### 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?_

[1]

The hyperparameter tuning process is explained for each hyperparameter. Also, the hyperparameters of the models are provided. The authors mention details about the process of obtaining and the budget in the appendix

### 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?_

[4]

The evaluation metrics are well explained. The evaluation procedure is explained. The data split is not clear and is mentioned in a scattered way. The data aggregation is explained.

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

[2]

The paper provides a good introduction to the topic in the introduction and relarted work sections. The models and the methodology of the experiments are explained well. Reader need to have some basic info about RNNs.
