Putting Bayes to Sleep
published: May 28, 2013, recorded: September 2012, views: 102
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In online multitask learning a single algorithm faces a collection of interleaved tasks. By addressing these tasks jointly rather than in isolation, the algorithm can discover and exploit similarities between tasks and hence learn faster and perform each task better.
We present a new method for multitask learning, built atop the "specialist" framework. We obtain a new intriguing efficient update that achieves a significantly better bound. Our method has linear efficiency and hence scales to very large data sets.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !