John Ioannidis - Reproducibility of scientific results |   | |   | |   |   |   |   |   | | John Ioannidis - Reproducibility of scientific results | | | | View on www.youtube.com | Preview by Yahoo | | | |   |

also see https://en.wikipedia.org/wiki/Ben_GoldacreBad Pharma - Wikipedia, the free encyclopedia

|   | |   | |   |   |   |   |   | | Bad Pharma - Wikipedia, the free encyclopediaBad Pharma: How Drug Companies Mislead Doctors and Harm Patients is a book by British physician and academic Ben Goldacre about the pharmaceutical industry, i... | | | | View on en.wikipedia.org | Preview by Yahoo | | | |   |

On Saturday, September 5, 2015 9:13 PM, David Warde-Farley d.warde.farley@gmail.com wrote:

I would argue that it's a social problem that is at least partly solved by enforcing community norms on archival publication venues, i.e. Reject anything that doesn't have compelling results on known public benchmarks (if there's other less reproducible stuff too, fine).If it's just people posting on arXiv, there's little to be done about it. We are better off having those unpublished reports in public view than not, though they are clearly a less compelling and less trustworthy form of evidence.On Sep 5, 2015 8:05 PM, "Yoshua Bengio" yoshua.umontreal@gmail.com wrote:

Yes. It's sad. 2015-09-05 19:44 GMT-04:00 Alex Lamb alex6200@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@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@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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