6 ms·
Neural networks meet space
- deleted 9y ago[deleted]
- nicklaf 9y agoNaive question: is the exponential increase in performance talked about in this article unique to neural nets? Or are there other techniques for writing classifiers that yield the same performance increase, given advances in hardware?
- mholt 9y agoNeural networks are function approximators. So if you 1) know an algorithm that is really computationally complex but not highly random and 2) have a lot of inputs and outputs of that algorithm, you can usually train a neural network to approximate a closed-form formula of the algorithm. It boils down to a bunch of matrix-multiplies and some standard non-linear functions in between.
- posterboy 9y agois that anything close to like polynomial fitting? What with PTIME and NP-Completion?
- mholt 9y agoKind of - but instead of computational complexity in the "NP" sense, you have lots of data. It's often so hard to get good training data that the cost of just waiting out a big algorithm to finish can be cheaper. So you have to weigh that.
- posterboy 9y agowell sure, you said as much before. But I was also thinking, what if P~=NP, in the sense that any function can be approximated by a polynomial of sufficiently high degree.
- jawbone3 9y agoNo, you can of course write some simple stretchy model that fits fast. The thing with NNs is that you don't have to domuch work to get a good fast model, you use cpu cores for that instead.
- hacker_9 9y ago'“The neural networks we tested—three publicly available neural nets and one that we developed ourselves—were able to determine the properties of each lens, including how its mass was distributed and how much it magnified the image of the background galaxy,” says the study’s lead author Yashar Hezaveh, a NASA Hubble postdoctoral fellow at KIPAC. This goes far beyond recent applications of neural networks in astrophysics, which were limited to solving classification problems, such as determining whether an image shows a gravitational lens or not.' Pretty fascinating stuff. Once you think about it, applying NNs to space makes a lot of sense. There is a ton of data to sift through and find patterns in. Amazing to think neural nets could crunch through this data in seconds, and point out areas of interest immediately. I wonder if NNs have been used in the search for exo planets yet.
- tachyonbeam 9y ago> I wonder if NNs have been used in the search for exo planets yet. Might be kind of overkill. The patterns being looked at for exoplanets are periodical dimming of stars AFAIK. I don't think you necessarily need a neural network to sift through that.
- jawbone3 9y agoDude, stars don't have constant brigthness...
- QAPereo 9y agoThey are also very very far away, and only a few photons are making it to the lens every so often; it's quite a difficult task.
- yh_82367 9y agoyes and no. Most exoplanet detections so far have been indirect: people just search for the dimming from the eclipse or similar other methods. So you're right about that. But recently we're starting to actually directly take sensitive enough images to be able to see planets (see http://planetimager.org http://planetimager.org). They aren't using NNs yet. But there have been discussions of using NNs for various aspects of those searches.
- hprotagonist 9y agodimensionality reduction is very powerful when you need to traverse a large configuration space and look for things that seem salient.
- m3kw9 9y agoWouldn’t it take too long to train with such amount of data? I’m thinking the NN has to be constantly be in training because you never want to miss some data sets that contain special categories and not to miss them when in the inference stage
- jawbone3 9y agoI guess you train it on things you know and use it to tell if a new observation looks familiar or not. You can't use the NN for the final analysis because its just a pile of linear algebra shaped like a causal theory: there is no actual physics in it.
- yh_82367 9y agoyes, in principle you're right. But in this case, the answer from the neural nets is so incredibly close to the true answer, that we think we can trust it for most purposes. If someone really wanted the most accurate answer, then they can start from the NN answer and do a proper model fitting procedure, which of course fits a simulated model with all the appropriate physics in it to the data.
- jawbone3 9y agoWell, in the important case of finding something new and interesting you need proper MC to verify your understanding. In practice you are right that using the NN like it actually speaks about reality will be common and not too harmful, people do lots of dubious least squares fits and astronomy still survives!
- yh_82367 9y agohi, Yashar here (one of the authors). These NNs were really fast to train. A day or so. We trained them on half a million simulated images. Then they're good to go for the analysis of any new data. We don't need to keep on training them as we get more data.
- jawbone3 9y ago
- thearn4 9y agoIf I understand it correctly, it sounds like they used a NN to fit a surrogate model to the kind of analytical physics-based pipeline that they had been using before?
- pfd1986 9y agoNice. Friends with the authors here, I'll try to bring them to answer questions.
- yh_82367 9y agoBob?