---
tags:
 - '#readingnotes'
 - '#projects/proposals'
 - '#machinelearning'
---

The factors that compromise the reproducibility of ML experiments are manifold including the following ones:
- Unavailability or outdated source code
- Unavailability of the datasets used for training and evaluation
- Unavailability of the reference implementation
- Unclear or missing description of the parameters that need to be set for obtaining the presented results
- Missing guidelines on the selection of the training, test and evaluation data
- Missing information of the required libraries/packages and their corresponding version
- Possible tweaks performed in the source code and that are not mentioned in the paper
- Missing information about the underpinning techniques, which have been used
- Lack of documentation about the preprocessing phases, like data preparation and cleaning
- Hardware requirements that could be not satisfied to train the large neural network