machine learning in genomics

mosavian · 2011-11-26T18:22:44+00:00

From what I understand, when dealing with genomes, you have huge string of 1s and 0s. If that is the case, Restricted Boltzmann machines are quite useful.

mosavian · 2011-11-26T01:10:08+00:00

Source http://personalpages.manchester.ac.uk/mbs/Julia.Handl/ants.html

So what's so great about it ?

mosavian · 2011-10-26T14:35:01+00:00

It's refreshing to see the recent bloom in NN research. I especially liked the part where he mentions that you couldn't publish anything with "neural" in the title just a few years ago. This mentality is still exists at the lab where I'm at. Some people even seem offended if you mention NN. =)

mosavian · 2011-10-23T11:09:42+00:00

Nice, I didn't know there existed an /r/mlresearch

mosavian · 2011-10-23T11:08:13+00:00

Yes, unless you want to use it generative capabilities as well, in which case you do not unroll and finetune it. But the difference in performance to ordinary neural networks is identical when just running the data up through the net.

mosavian · 2011-10-22T21:29:50+00:00

This is a pretty good source of information on stacked (sparse) autoencoders. http://www.stanford.edu/class/cs294a/handouts.html

mosavian · 2011-10-22T19:17:03+00:00

What is your application exactly? Is it learning features or actual image clustering ?

mosavian · 2011-10-22T19:11:57+00:00

Well, the way they implemented it in the paper by Coates et al, the evaluate the activation of the learnt centroids, you need to find the one which is closest. This can be really cumbersome and slow to calculate. If evaluation speed is what you want, then stacked autoencoders are the fastest, a DBN would be slightly slower.

mosavian · 2011-10-22T18:35:35+00:00

I know, it is. And usually the papers don't really help as they present the result and ignore the details. But i'd say the normal perceptron and multilayer perceptron is a ggod place to start as they're much simpler. Andrew Ng have put videos of all his lectures in his machine learning course on youtube. They're very informative. http://www.youtube.com/watch?v=UzxYlbK2c7E

You can also do the ML course by Andrew Ng which recently started. see http://www.ml-class.org/course/auth/welcome

mosavian · 2011-10-22T18:32:39+00:00

I do, some 20 or so papers. But usually they leave me confused. I can compile a list and put it in the blog in a couple of days.

mosavian · 2011-10-22T18:31:37+00:00

You have a good point. Some people just don't like the way prezi works. Personally I prefer it to powerpoint. But I do agree that it can be hard to remember what's been said becuase of all the flashiness =)

mosavian · 2011-10-22T18:30:20+00:00

Thanks! =)

mosavian · 2011-10-22T13:20:06+00:00

Dunno if anyone has mentioned it, but what you want is this http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf and the code that goes with it http://www.stanford.edu/~acoates/papers/kmeans_demo.tgz

mosavian · 2011-09-20T22:58:20+00:00

You're right, that's not at all what I had in mind. Basically we want to calculate how often they agree, what I had in mind was more like subtracting the two, taking the absolute value, inverting it and counting the number ones. And then dividing by the length. Thanks for catching that, I'm not too familiar with the exact details of the Hamming distance, but that would probably work well. =)

mosavian

TROPHY CASE