tag:blogger.com,1999:blog-6141980.post8387653253760597862..comments2024-03-20T12:28:35.004-05:00Comments on Nuit Blanche: #ICLR2017 Tuesday Morning Program Igorhttp://www.blogger.com/profile/17474880327699002140noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-6141980.post-31426580021222435732017-04-26T00:38:09.441-05:002017-04-26T00:38:09.441-05:00I think what is happening is that you are getting ...I think what is happening is that you are getting unsupervised feature learning in the deeper layers and then one final readout layer. That may give a boost in performance in some circumstances. There probably are better ways to do unsupervised feature learning prior to a readout layer. There are also some aspects to do with noise and maybe some cooling effect over time as the system adapts. Thumbs up or thumbs down, I don't know. You be the judge. <br /> <br />SeanVNhttps://www.blogger.com/profile/05967727000105480078noreply@blogger.comtag:blogger.com,1999:blog-6141980.post-83470436554465103092017-04-25T23:45:22.461-05:002017-04-25T23:45:22.461-05:00Re: https://openreview.net/pdf?id=HkXKUTVFl
I'...Re: https://openreview.net/pdf?id=HkXKUTVFl<br />I'm trying dropout in relation to the back error projection. Anyway there are tons of ideas to explore, especially if you start using fast random projection algorithms for both the back error projection and the forward aspects of a network.SeanVNhttps://www.blogger.com/profile/05967727000105480078noreply@blogger.com