This may be just what I needed:
I am pleased to announce that Foundations and Trends in Machine Learning (www.nowpublishers.com/mal) has published the following issue:
Volume 9, Issue 2-3
Patterns of Scalable Bayesian Inference
By Elaine Angelino (University of California, Berkeley, USA), Matthew James Johnson (Harvard University, USA) and
Ryan P. Adams (Harvard University and Twitter, USA)
The link will take you to the article abstract. If your library has a subscription, you will be able to download the PDF of the article.
If you do not have access, download the free preview here: http://www.nowpublishers.com/article/DownloadSummary/MAL-052
To purchase the book version of this issue, go to the secure Order Form:
You will receive the alert member discount price of $40 (includes shipping) by quoting the Promotion Code: 318306
This issue is also available for purchase at this year’s NIPS conference. Visit our booth to view all of the latest FnT ML titles.
a software inspection process called GenderMag. You can try it for yourself. The process is freely available, and major technology companies are looking at the possibility of adopting it.
You can make GitHub repositories archival by using Zenodo or Figshare!
Wed 16 November 2016
By C. Titus Brown
Bioinformatics researchers are increasingly pointing reviewers and readers at their GitHub repositories in the Methods sections of their papers. Great! Making the scripts and source code for methods available via a public version control system is a vast improvement over the methods of yore (“e-mail me for the scripts” or “here’s a tarball that will go away in 6 months”).
Hay spoke of the difficulty of conveying the uncertainty that goes along with
these predictions. For example, his team spends half of its time developing the
correct uncertainty envelopes for the maps, and he does not have a good idea
on how to communicate this uncertainty to the many constituencies that would
find the maps useful. One aspect of these maps that he finds particularly vexing
is the tendency for people to just look at the map and ignore all of the richer
detail about uncertainty that his team provides with the maps.
I was nearly convinced that Google’s TensorFlow would take over the world, but now I’ll need to also consider MXNet: http://mxnet.io/