[Lisa_seminaires] [Lisa_labo] Fwd: Connectionists: The ReScience journal

Yoshua Bengio yoshua.umontreal at gmail.com
Sam 5 Sep 20:05:31 EDT 2015


Yes. It's sad.

2015-09-05 19:44 GMT-04:00 Alex Lamb <alex6200 at gmail.com>:

> A lot of the papers that I've read recently use a private training set and
> a public testing set (for example, the Google and Facebook face recognition
> papers).  In areas where the largest datasets are privately held, research
> is probably going to continue to be impossible to fully reproduce.
>
> On Sat, Sep 5, 2015 at 7:03 PM, David Krueger <
> david.scott.krueger at gmail.com> wrote:
>
>> To what extent can this be remedied by using aws or similar?
>> On Sep 5, 2015 7:00 PM, "David Warde-Farley" <d.warde.farley at 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 at 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 at inria.fr>
>>> > Date: 2015-09-03 8:57 GMT-04:00
>>> > Subject: Connectionists: The ReScience journal
>>> > To: Connectionists group <connectionists at 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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>>> >
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>>
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