Brains, Sex, and Machine Learning

What people said

eveningcoffee · 2015-11 · Strong recommendation

I would highly recommend this video [VIDEO] from Geoffrey Hinton in 2012 that I think is related to this research.

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pilooch · 2013-05 · Strong recommendation

It seems the relationship between the brain and deep learning has evolved in such a way that the later can help with insights into how the former works. In this regard, I thought I would mention the extraordinary simple and elegant talk by G. Hinton last summer: [VIDEO] It starts from a simple and clever improvement to an existing deep learning method and ends up with beautiful (and simple!) insights on why neurons are using simple spikes to communicate.

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FrojoS · 2012-11 · Strong recommendation

Here is a GREAT talk by Geoffrey Hinton (the Prof running said lab) [VIDEO] where he explains the method. Unfortunately, even though it was posted three times to HN [LINK] it never made the fron page. Here is the my summary and comment: " Great talk. I don't know much about artificial neural networks (ANN) and even less about natural ones, but I have the feeling that I learnt a lot from this video. If I understand correct, Hinton uses so many artificial neurons compared to the amount of learning data, that you would usually see an overfitting effect. However, his ANN's randomly shut of a substantial part (~50%) of the neurons during each learning iteration. He calls this "dropout". Therefore, a single ANN represents many different models. Most models never get trained, but they exist in the ANN, because they share their weights with the trained models. This learning method avoids over specializing and therefore improves robustness with respect to new data but it also allows for arbitrary combination of different models which tremendously enlarges the pool of testable models. When using or testing these ANNs you also "dropout" neurons during every prediction. Practically, every rerun predicts a different result by using a different model. Afterwards, these results are averaged. The more results, the higher the chance, that the classification is correct. Hinton argues, that our brains work in a similar way. This explains among other things a) Why are neurons firing in a random

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