Explaining code for publication

By Harold W. Thimbleby. 2003

37 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Information Systems and Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Ming-Hsuan Yang
4. Trevor Darrell
5. Andrea Vedaldi
6. Xiaogang Wang
7. Yoshua Bengio
8. Jitendra Malik

In Information Systems

1. Robert C. Williamson
2. G. Holmes
3. K. Müller
4. Jason Aaron Edward Weston
5. Fernando C Pereira
6. Pascal Vincent
7. Yann LeCun
8. Cheng Soon Ong

In Information Systems and Management

1. Bernhard Schölkopf
2. Andreas Holzinger
3. S. Lo Piano

Citations in the corpus

• Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, methodology): “On a larger scale, the use of open-source software in the context of ML applications has already been advocated for over a decade (Thimbleby, 2003) with an indirect call for tools to execute more interpretable and reproducible programming such as Jupyter Notebooks , available from 2015 onwards.”
• The Need for Open Source Software in Machine Learning (2007, background): “However, few machine learning researchers currently publish the software and/or source code associated with their papers (Thimbleby, 2003).”
• Introduction to MAchine Learning & Knowledge Extraction (MAKE) (2017, background): “• (1) availability of open source code associated with research papers [117]; • (2) reproducibility of available methods and tools which is a cornerstone in fundamental science; • (3) usability and usefulness of that code for solving real-world problems.”