# A Guide to Computational Reproducibility in Signal Processing and Machine Learning

arXiv: https://arxiv.org/abs/2108.12383 · HTML: https://mlsanity.com/paper/2108.12383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [A Step Toward Quantifying Independently Reproducible Machine Learning Research](https://mlsanity.com/paper/1909.06674/) (2019)
- [An introduction to Docker for reproducible research, with examples from the R environment](https://mlsanity.com/paper/1410.0846/) (2014)
- [Computing Environments for Reproducibility: Capturing the "Whole Tale"](https://mlsanity.com/paper/1805.00400/) (2018)
- [Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms](https://mlsanity.com/paper/1903.09284/) (2019)
- [Reproducible Research: Best Practices and Potential Misuse](https://mlsanity.com/paper/1905.00645/) (2019)
- [Publishing computational research -- A review of infrastructures for reproducible and transparent scholarly communication](https://mlsanity.com/paper/2001.00484/) (2020)

## Built on by (most cited first, 1 of 1)

- [Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression](https://mlsanity.com/paper/2305.16534/) (2023)

## Abstract (arXiv)

Computational reproducibility is a growing problem that has been extensively studied among computational researchers and within the signal processing and machine learning research community. However, with the changing landscape of signal processing and machine learning research come new obstacles and unseen challenges in creating reproducible experiments. Due to these new challenges most computational experiments have become difficult, if not impossible, to be reproduced by an independent researcher. In 2016 a survey conducted by the journal Nature found that 50% of researchers were unable to reproduce their own experiments. While the issue of computational reproducibility has been discussed in the literature and specifically within the signal processing community, it is still unclear to most researchers what are the best practices to ensure reproducibility without impinging on their primary responsibility of conducting research. We feel that although researchers understand the importance of making experiments reproducible, the lack of a clear set of standards and tools makes it difficult to incorporate good reproducibility practices in most labs. It is in this regard that we aim to present signal processing researchers with a set of practical tools and strategies that can help mitigate many of the obstacles to producing reproducible computational experiments.