I agree and I was basically thinking the same thing (+that Docker or other container virtualization techniques could ease the pain [*]).

But for reproducability in the scientific sense it is already a huge win when an independent implementation ran only once or twice on the machines of the reproducing authors (and the editors). From that point of view it would not be devastating if the code was not in a runnable state a few months later.

   j

[*] I actually wonder why they did't make the dependencies more explicit in their submission format.

On Sat, Sep 5, 2015 at 7:00 PM David Warde-Farley <d.warde.farley@gmail.com> wrote:

Very encouraging to see this happening and that other people are
concerned about it.

I would add that reproducibility in machine learning looks simple
compared to other scientific domains, but looks are deceiving. Every
"simple Python script" is built upon a broad and deep tower of library
dependencies, leading to an exponential number of ways that your
computing environment can conspire against you (nevermind hardware
differences...).

On Sat, Sep 5, 2015 at 6:35 PM, Yoshua Bengio
<yoshua.umontreal@gmail.com> wrote:
>
> Very interesting!
> Reproducibility is VERY weak in the machine learning community, and needs to
> be improved.
>
> ---------- Forwarded message ----------
> From: Nicolas P. Rougier <Nicolas.Rougier@inria.fr>
> Date: 2015-09-03 8:57 GMT-04:00
> Subject: Connectionists: The ReScience journal
> To: Connectionists group <connectionists@cs.cmu.edu>
>
>
>
> It's our great pleasure to announce the creation of "ReScience" which is a
> peer-reviewed journal that targets computational research and encourages the
> explicit replication of already published research, promoting new and
> open-source implementations in order to ensure that the original research is
> reproducible.
>
> To achieve such a goal, the whole editing chain is radically different from
> any other traditional scientific journal. ReScience lives on GitHub where
> each new implementation is made available together with comments,
> explanations and tests. Each submission takes the form of a pull request
> that is publicly reviewed and tested in order to guarantee that any
> researcher can re-use it.
>
> Students are strongly encourage to submit to ReScience. Even if the
> publishing model is a bit different from other academic journals, this will
> give them a first experience at peer-reviewed publishing where they have to
> use a rigorous and scientific approach.
>
>         • More on the journal website:
> https://github.com/ReScience/ReScience/wiki
>         • Current issue:
> https://github.com/ReScience/ReScience/wiki/Current-Issue
>         • FAQ:
> https://github.com/ReScience/ReScience/wiki/Frequently-Asked-Questions
>         • Follow us on twitter (@ReScienceEds):
> https://twitter.com/rescienceeds
>
> And if you're familiar with Git and GitHub, you can also become a reviewer:
> just contact us.
>
>
> Konrad Hinsen & Nicolas Rougier
>
>
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