Re: [Lisa_seminaires] [Lisa_labo] Fwd: Connectionists: The ReScience journal
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
Lisa_labo mailing list Lisa_labo@iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo
Lisa_labo mailing list Lisa_labo@iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo
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Important to distinguish replication and reproduction. Replication is (in my opinion) when experiments in similar or same environments work. Reproduction is more independent - implementing the same ideas in a very different setting. This uncovers much more subtle (but important) issues than basic replication. And we need both in ML - but replication doubly so.
Also dwf's point is a strong reason to minimize dependencies in research and library development. Its a personal opinion but one that has helped greatly in the past. On Sep 5, 2015 7:18 PM, "Jörg Bornschein" bornj@iro.umontreal.ca wrote:
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
Lisa_labo mailing list Lisa_labo@iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo
Lisa_labo mailing list Lisa_labo@iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo
Lisa_seminaires mailing list Lisa_seminaires@iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_seminaires
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