On Wed, Jun 27, 2012 at 2:09 AM, bfrs <[email protected]> wrote: > nytimes article on this paper: > https://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html?_r=1
Original paper here: http://arxiv.org/pdf/1112.6209v3.pdf To summarize, a 9 layer neural network with 10^9 connections is trained unsupervised for 3 days on 1000 16-core CPUs on 10^7 unlabeled 200x200 images, each a random frame from a different Youtube video. When the resulting top level neurons are examined, it turns out that there are detectors for (among other things) human faces, human bodies, and cats. It was not told to look for these things. This is just a compression problem. If you want to encode an image efficiently, then you do so by describing its high level features (e.g. a person holding a cat). The learning problem is to find a set of useful features, knowing nothing about the world or what these arrays of pixels might represent. It does not achieve human level accuracy, but is still better than anything else. The equivalent problem for human vision would be to train 10^13 synapses for a decade on 10^9 images of 10^8 pixels each. -- -- Matt Mahoney, [email protected] ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
