[mstoehr]

I'm not any sort of expert on the neural network literature but these were some papers in the last three years that caught my eye, Yann LeCun also does work on neural nets, but I haven't been all that impressed by his results. One of the main advances has been ways of developing 'deep' architectures with multiple layers rather than the traditional shallow neural networks (arguably the SVM, for instance, is actually a very cleverly trained single layer neural network)

Here's a Geoffrey Hinton paper on training deep belief networks: http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf

Here's some stuff from Andrew Ng's group: This paper shows how his deep belief network was able to 'learn' in an unsupervised manner certain plausible image primitives http://robotics.stanford.edu/~ang/papers/nips07-sparsedeepbe... This won a best paper award (application paper) and its about fast ways of building a deep belief network: http://robotics.stanford.edu/~ang/papers/icml09-Convolutiona...

[varjag]

Thanks a lot for the references, now I have some reading for tomorrow!