L. Estève will give a talk on scikit-learn at Inria Lille on April 27th.
This release has new features, enhancements and bug fixes and some improvements in the documentation and refactoring of examples.
Thanks to everybody for their contributions in this release.
More detailed information here: http://nilearn.github.io/whats_new.html
“Tracking the Evolution of Dynamic Networks”
See more information here: https://www.inria.fr/centre/saclay/agenda/seminaire-du-departement-stic-avec-francois-meyer.
L. Estève will give a talk on scikit-learn at CEA DAM on April 6th.
If you’ve completed your PhD thesis less than one year ago and want to spend 16 months in a collaboration between Parietal and Poldrack’s lab at Stanford, this is a great opportunity:
***Deadline on April 14th***
L. Estève will give a talk about continuous integration in scikit-learn at a LoOPS workshop on March 27th. More details at http://www.reseau-loops.github.io/journee_2017_03_IntegrationContinue.html.
We are pleased to announce the new 0.14 release of MNE-Python. As usual this release comes with new features, bug fixes, and many improvements to usability, visualization, and documentation.
More information here: http://martinos.org/mne/stable/whats_new.html#whats-new
Some of our best members will part to the MNE-Python coding sprint in New York next week:
Go go team !
Gael will give a talk at the next Neurospin unsupervised decoding meeting on 21/02/2017 – 9h45-11h00, room 2032
Abstract: Recently, I have become convinced that cross-validation on a hundred or
less samples is not a reliable measure of predictive accuracy. In
addition, techniques generally used to estimate its error or test for
significant prediction are severely optimistic.
I would like to present you very simple evidence of this unreliability,
which it intrinsic to the sample sizes that we are working with. It is a
simple sampling-noise problem that cannot be alleviated without increasing
the number of samples.
I want to have a discussion about what this means for the field, and how
we should address this problem. I would like to invite critical thinking
about aspects of the practice that I might have overlooked and could make
it more robust.
I invite many people to come, so that we can convince ourselves of
whether or not there is a problem with the way we often work. Indeed, it
is troublesome for methods development as well as for neuroscience
Bertrand takes part to the table ronde “Big data & health” on January 28th at La Cité des Sciences, Paris.