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Peering into neural networks

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Re: Peering into neural networks

#4
I like it. I've seen experiments that break out eigenvectors of a neural network, which is like being given a dictionary in a foreign language. It's precise, but you still have to figure out what each eigenvector means. This technique is like having a translating dictionary. It's less precise but it lets you reason about the network with a familiar visual vocabulary.

Re: Peering into neural networks

#5
post #2

Question to the community. Where can i follow research on this area understanding decision making and reasoning of neural networks?

Well, I think the research is more ad-hoc than being it's own field at this point.

I just scan papers that come up in the Reddit group[1]. I've seen:

"Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks" by Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum

"Rationalizing Neural Predictions" by Tao Lei, Regina Barzilay and Tommi Jaakkola

"'Why Should I Trust You?' Explaining the Predictions of Any Classifier by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin

You might be able to chase down the works of these various authors to find more.

[1] https://www.reddit.com/r/MachineLearning/

Re: Peering into neural networks

#7
1.) For the umpteenth time, they're not black boxes. We can inspect everything in the structure.

2.) "a team of computer-vision researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL)" may have "described a method for peering into" the not-black box of a convnet two years ago, but Oxford researchers published on in in 2013.

3.) Gushing about how understanding convolutional networks can help confirm the grandmother cell hypothesis in real brains is embarrassing under all circumstances but should be particularly so when thorough examinations from real brains just came out to the considerable detriment of said hypothesis. http://www.cell.com/cell/fulltext/S0092-8674(17)30538-X

Nothing wrong with making visualizations of your nets, but I'm less than impressed by the reporting.

Re: Peering into neural networks

#9
post #8

I wonder if our understanding of neural networks will help us understand the human brain.

If THAT happens, then we can - after some further research - loose the last bits of our privacy - our mind...

Maybe one day. From my understanding we're not even close. We don't even have the computing power available to simulate a human brain. But yeah, scary applications. Minority Report anyone?

Re: Peering into neural networks

#10
post #7

1.) For the umpteenth time, they're not black boxes. We can inspect everything in the structure. 2.) "a team of computer-vision researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL)" may have "described a method for peering into" the not-black box of a convnet two years ago, but Oxford researchers published on in in 2013. 3.) Gushing about how understanding convolutional networks can…

Few things are black boxes in absolute terms. It's the difficulty of understanding NN's that makes them black boxes. Personally I think the black box analogy is accurate for any net of appreciable size.
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