Can you point me to an overview of this understanding? I would like to apply it to the problem of detecting different types of data in a raw binary file.
I don’t know of a good one. You could try this, but it’s light on the math. I’m looking through Thrun’s papers to find a good one that gives a simple overview of the concepts, and through the CES documentation.
I was introduced to this advancement in EY’s Selling nonapples article.
And I’m not sure how this helps for detecting file types. I mean, I understand generally how they’re related, but not how it would help with the specifics of that problem.
Thanks I’ll have a look. I’m looking for general purpose insights. Otherwise you could use the same sorts of reasoning to argue that the technology behind deep blue was on the right track.
True, the specific demonstration of Thrun’s that referred to was specific to navigating a terrestrial desert environment, but it was a much more general problem than chess, and had to deal with probabilistic data and uncertainty. The techniques detailed in Thrun’s papers easily generalize beyond robotics.
I’ve had a look, and I don’t see anything much that will make the techniques easily generalize to my problems (or any problem that has similar characteristics to mine, such as very large amounts of possibly relevant data). Oh, I am planning to use bayesian techniques. But easy is not how I would characterize the translating of the problem.
Now that you mention it, one of the reasons I’m trying to get acquainted with the methods Thrun uses is to see how much they rely on advance knowledge of exactly how the sensor works (i.e. its true likelihood function). Then, I want to see if it’s possible to infer enough relevant information about the likelihood function (such as through unsupervised learning) so that I can design a program that doesn’t have to be given this information about the sensors.
And that’s starting to sound more similar to what you would want to do.
That’d be interesting. More posts on the real world use of bayesian models would be good for lesswrong I think.
But I’m not sure how relevant to my problem. I’m in the process of writing up my design deliberations and you can judge better once you have read them.
The reason I say that our problems are related is that inferring the relevant properties of a sensor’s likelihood function looks like a standard case of finding out how the probability distribution clusters. Your problem, that of identifying a file type from its binary bitstream, is doing something similar—finding what file types have what PD clusters.
Can you point me to an overview of this understanding? I would like to apply it to the problem of detecting different types of data in a raw binary file.
I don’t know of a good one. You could try this, but it’s light on the math. I’m looking through Thrun’s papers to find a good one that gives a simple overview of the concepts, and through the CES documentation.
I was introduced to this advancement in EY’s Selling nonapples article.
And I’m not sure how this helps for detecting file types. I mean, I understand generally how they’re related, but not how it would help with the specifics of that problem.
Thanks I’ll have a look. I’m looking for general purpose insights. Otherwise you could use the same sorts of reasoning to argue that the technology behind deep blue was on the right track.
True, the specific demonstration of Thrun’s that referred to was specific to navigating a terrestrial desert environment, but it was a much more general problem than chess, and had to deal with probabilistic data and uncertainty. The techniques detailed in Thrun’s papers easily generalize beyond robotics.
I’ve had a look, and I don’t see anything much that will make the techniques easily generalize to my problems (or any problem that has similar characteristics to mine, such as very large amounts of possibly relevant data). Oh, I am planning to use bayesian techniques. But easy is not how I would characterize the translating of the problem.
Now that you mention it, one of the reasons I’m trying to get acquainted with the methods Thrun uses is to see how much they rely on advance knowledge of exactly how the sensor works (i.e. its true likelihood function). Then, I want to see if it’s possible to infer enough relevant information about the likelihood function (such as through unsupervised learning) so that I can design a program that doesn’t have to be given this information about the sensors.
And that’s starting to sound more similar to what you would want to do.
That’d be interesting. More posts on the real world use of bayesian models would be good for lesswrong I think.
But I’m not sure how relevant to my problem. I’m in the process of writing up my design deliberations and you can judge better once you have read them.
Looking forward to it!
The reason I say that our problems are related is that inferring the relevant properties of a sensor’s likelihood function looks like a standard case of finding out how the probability distribution clusters. Your problem, that of identifying a file type from its binary bitstream, is doing something similar—finding what file types have what PD clusters.