81–90 of 146 posts
I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the lack of mathematics . Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much…
I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the lack of mathematics . Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much…
Why is it that Neural Net-based ML only seems to be claiming results with images and natural language? Maybe I'm out of the loop, but I haven't seen anything demonstrating results on "data" – the kinds of challenges that are actually valuable to businesses. Why is that? Are those just less sexy / more proprietary in nature, or is there something about those challenges that make NN's less useful to them?
In fact I remember talking to an ML guy with a PHD who was working on one of these types of problems and I asked "why not try NNs on this problem". He looked at me with disgust and said something akin to "it's provable that NNs can never do better than BOOST, so why use them?"
However, boosted decision trees don't work on image analysis at all, so these types of problems have become the standard for NNs.
It is also worth noting that people are more likely to try image problems if everyone else is trying image problems, because then it is easy to compare multiple algorithms together.
I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the lack of mathematics . Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much…
So what we're seeing is these fields pre-deep learning were mathematical disciplines making steady progress on well-understood foundations. Then deep learning came in, was exceptionally effective at problems that had been difficult to crack, and people shifted focus because it seems weird to be diddling around with incremental gains on techniques that are significantly less effective. What this created though, was a…
I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the lack of mathematics . Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much…
This shows that computers soon will have the ability to fool our senses so well that we may not even believe reality when it is right in front of us. Some of the pictures, when I was just viewing them (before reading captions or titles) looked real. I was astounded to see that they were derivatives from paintings.
The implications are significant, not just in things like gaming, or finance, but especially in psychology, where the delicate aspects of the mind may be easily disrupted. I expect there will be numerous growth in neuroses over the coming decades. Technology will have surpassed natural evolution by such a margin, that it could be difficult to recover.
I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the lack of mathematics . Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much…
My one takeaway from this is that the future will be a scary place. This is impressive work, so please don't take that as a knock against this phenomenal work. This shows that computers soon will have the ability to fool our senses so well that we may not even believe reality when it is right in front of us. Some of the pictures, when I was just viewing them (before reading captions or titles) looked real. I was asto…