2 ms·

Short, massively simplified, summary, Vast improvement in unsupervised learning. Walks through biases humans have in object recognition. Explains new structur

by jfoutz 12y ago

Short, massively simplified, summary, Vast improvement in unsupervised learning. Walks through biases humans have in object recognition. Explains new structures to add on top of neural networks. Basic backprop on MNIST with 30,000 labeled examples gives 1.7% error rate. Hinton's approach gets the same results with 25 examples.

dchichkov 12y ago
With 25 examples on which the systems asks the labels (and unknown number of unlabeled examples). ... liked the progress on reverse computer graphics approach. A bit of personal perspective here, from the data that I saw (and labeled!), while working on hand gesture recognition project the reverse computer graphics felt like the only approach that could bring human level gesture recognition from 2d data. And I was really exited at some point when Nvidea had started a computer vision / gesture recognition group. It felt really right that a photographic-level computer graphics GPU company would try to do reverse computer graphics processor. Too bad it didn't go anywhere.