> We propose two new metrics: the Written In Latex (WIL) metric measures the proportion of a machine learning library that is written in pure LATEX, while the Source Code Of Method in Source Code of Paper (SCOMISCOP) metric measures the proportion of a paper’s implementation that is contained within the paper source. We are state-of-the-art for both metrics [...]
I've seen legit ML papers propose worse metrics :)
Must be a late entry.. Unfortunatel the WIL metric overlaps in name with Written in Lisp:
Additionally we propose two new metrics. The Written in Latex (WIL) metric is the proportion of source code of a machine learning library written in LaTeX and the Source code of method in source code of paper (SCOMISCOP) metric is the proportion of the source code of a method that is contained within the source code of the paper.
> We propose two new metrics: the Written In Latex (WIL) metric measures the proportion of a machine learning library that is written in pure LATEX, while the Source Code Of Method in Source Code of Paper (SCOMISCOP) metric measures the proportion of a paper’s implementation that is contained within the paper source. We are state-of-the-art for both metrics [...]
I've seen legit ML papers propose worse metrics :)
Must be a late entry.. Unfortunatel the WIL metric overlaps in name with Written in Lisp:
Additionally we propose two new metrics. The Written in Latex (WIL) metric is the proportion of source code of a machine learning library written in LaTeX and the Source code of method in source code of paper (SCOMISCOP) metric is the proportion of the source code of a method that is contained within the source code of the paper.
I love this thing.