
Stochastic Pattern Recognition Dramatically Outperforms Conventional Techniques - llambda
http://www.technologyreview.com/blog/arxiv/27598/
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onemoreact
Wow, that's poor reporting. _Stochastic Pattern Recognition_ can be useful,
but that article misrepresents the advantages. Just read the paper
arxiv.org/abs/1202.4495 if you want to understand what this is about.

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ArbitraryLimits
Are probabilistic techniques _still_ not considered conventional? It's not
like the mathematical foundations of probability haven't been laid hundreds of
years ago (I'm talking about the rules of inference, not the axiomatization).

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NyxWulf
I believe that was in reference to the construction of logic gates. In that
context I don't think probabilistic gates are considered conventional.

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bravura
Yes they are: bayesian neural networks can model stochastic logic gates, and
are used for pattern recognition.

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stochastician
This is similar to work myself and Vikash Mansinghka were doing in 2008 --
[http://dspace.mit.edu/bitstream/handle/1721.1/43712/MIT-
CSAI...](http://dspace.mit.edu/bitstream/handle/1721.1/43712/MIT-CSAIL-
TR-2008-069.pdf) . It's actually the basis of my PhD thesis, "Stochastic
Architectures for Probabilistic Computation" -- if only I wasn't so busy with
this startup!

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erichocean
Hmm. Unbiased rendering is evaluating PDFs millions of times per second. Do
you think stochastic logic could be used to speed that process up?

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Geee
If I have understood this whole thing right, stochastic logic would also be
much tolerant to manufacturing errors on silicon, low voltages, high clock
rates etc. which would allow cheaper, smaller and more power efficient signal
processing. Great deal of energy is consumed on current chips to make sure
that every bit is just right.

I'd also like to know if there's any good sources for learning more about
this.

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HPBEggo
This actually seems VERY important, although probably less so if you've been
exposed to the idea before.

Regardless, the results are impressive, and the ability to impose the
mathematical properties' of PDFs or CDFs over those of discrete numbers could
have an enormous impact on the efficiency of certain types of algorithms.

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lkrubner
Imagine if, for any given position in a list, there were only 4 values
allowed:

A

T

G

C

What is the value of the next item in the list? Pure guessing and you have a
25% chance of being right. But what if a review of past lists allows you to
develop weighted averages, to the point where you could be, say, 70% correct?
What if there was a way to do multiple scans, such that the chance of
correctness gets to be some value of 99.x% ?

Imagine the 4 values stand for:

Adenine

Thymine

Guanine

Cytosine

It seems to me there is here suggested a new way of parsing a genome. I have
friends who've worked on automated cancer detection based on recognizing
certain patters in photos of cells. Possibly similar techniques could take
electron microscope scans and figure out a sequence of DNA?

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ArbitraryLimits
Well, this is actually the old way of parsing a genome. What you've described
is essentially a Hidden Markov Model with discrete states, which is the bread
and butter of genome sequencing.

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a1k0n
I'm actually curious about that distribution; could you gain compression
efficiency by grouping them into 3-base-pair codons? Or is DNA pretty much
random at the base-pair level and the codon redundancy makes it actually work
anyway?

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kghose
The description on the page reminded me about how nervous systems might
compute. Neural firing is stochastic on small time scales. People fight over
whether the fine timing between spikes is important for computation, but
largely it seems rates of firing are important.

But, we know that the nervous system has coincidence detectors - basically AND
gates for spikes.

This article got me excited because I never thought of coincidence detectors
as performing multiplication - an operation very hard to thing about in neural
circuitry terms.

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jlluis
Stochastic computing is not used by current digital technologies. About the
processing in the brain is complex to know exactly what's happens there.
Probably there is a synergy between chaotic and ordered behaviors. In Nature
Procedings you can find a pre-print paper of the same authors talking about
this point. <http://precedings.nature.com/documents/6935/version/1>

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nightski
I have heard of this before and was rather excited to see further
developments. But am I reading this right? The stochastic processor is 70x
slower than a conventional processor. The only reason they achieve 3x faster
is due to "parallel" processing? The details are vague, but does the speedup
have anything to do with the fact that the processor is non-deterministic?

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kal00ma
Has anyone come across an emulator/simulator for experimenting with stochastic
logic?

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cammil
This is so the future. Determinism is over rated.

