
## Entropy Estimation via Normalizing Flow
Ziqiao Ao, Jinglai  Li
Keywords: Reasoning Under Uncertainty (RU)
AAAI/2022/Proceedings/21237 - Entropy Estimation via Normalizing Flow.pdf

### 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?_

[10]

The authors do not provide details on their implementation.

### 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?_

[5]

(2/2)

The authors use a perfect NF transformation with a normal distribution as data to model. 

### 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?_

[8]

The authors method uses kNN but k is not specified.

### 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?_

[3]

The authors specify the two tests they conduct, and the amount of times its repeated. They use RMSE as a metric. However, the experiments conducted are highly complex so to fully follow the steps done by the authors requires quite some expertise.

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

[10]

This method requires high expertise regarding entropy and statistics. To fully reproduce the method, regardless of the absent implementation, requires a lot of extra materials to be studied to fully grasp the concept presented here.
