>if you have a problem with big stationary correlational structure in the inputs-- >you should transform it out so NuPIC can work on the non obvious features
Agreed. In some sense this is what pooling does. The spatial pooler transforms spatially correlated (but very different) patterns into a consistent SDR pattern. The temporal pooler transforms temporally predictable (but potentially totally different) SDR patterns into a single coherent SDR representation. The encoders also can help greatly with this. --Subutai On Fri, Sep 26, 2014 at 12:01 AM, Archie, Kevin <[email protected]> wrote: > Subutai, > > Thanks for the link, interesting reading. I'm amused by the problem of > it being (1) a problem NuPIC isn't really well suited to and (2) the first > thing lots of people are going to try anyway. > > Thinking about statistics, it seems to me that if you have a problem > with big stationary correlational structure in the inputs--either temporal, > as the sine wave example, or spatial, as John's bridge data--you should > transform it out so NuPIC can work on the nonobvious features. Thinking > about biology, by the time you get to cortex a lot of the stationary > structure has been filtered out -- think about the processing in the > retina. It's possible that NuPIC is good enough to solve lots of > interesting problems even without preprocessing, but I suspect that some > care to the input representation could greatly help with performance (in > cycles, at least by reducing the input dimensionality) and performance (in > error rate, by getting rid of lots of chaff). > > Or I could be all wet. It happens. > > - k > > On Sep 25, 2014, at 1:53 PM, Subutai Ahmad wrote: > > Hi Kevin, > > I did some simple experiments with swarming and correlated inputs [1]. > One thing to note is that temporal correlation / sequence structure is also > very important. That is independent from spatial correlation. > > --Subutai > > [1] https://github.com/subutai/nupic.subutai/tree/master/swarm_examples > > On Thu, Sep 25, 2014 at 11:33 AM, Archie, Kevin <[email protected]> > wrote: > >> John, >> >> I think this is an example of an important general case. On both >> statistical and biological grounds I suspect you should decorrelate your >> inputs before producing SDRs from them. I haven't tried this myself (have >> hardly done anything with NuPIC) but I'm wondering if anyone has done >> substantial preprocessing to compensate for the statistics of the inputs. >> Even better would be if anyone has done a comparison of feeding NuPIC >> decorrelated vs. direct sensor inputs--or a theoretical argument that >> decorrelating the inputs is unnecessary or unwise. >> >> - Kevin >> >> ------------------------------ >> *From:* nupic [[email protected]] on behalf of John >> Blackburn [[email protected]] >> *Sent:* Tuesday, September 23, 2014 7:27 AM >> *To:* Archie, Kevin >> *Subject:* Re: Which NuPIC tutorial do you want to see next? >> >> Hi Matthew, >> >> Rather self serving, but I would love to see a tutorial related to the >> "bridge" simulation I've been trying to do or similar. I have not got NuPIC >> to work yet despite some effort. Basically the difference with Hotgym is we >> have 18 sensors, 10 temperature and 8 tilt (ie strain) and we want to make >> predictions on all taking account of cross-correlations. So a tutorial with >> multiple correlated time series would be great! >> >> At NPL we monitored a bridge every 5 minutes for 3 years recording all >> 18 sensors so I think this data would be a great showcase for a real-world >> NuPIC example. We also perturbed the bridge at known times (cutting >> supports, adding weights etc) so we know when the anomalies should appear. >> >> John. >> >> On Mon, Sep 22, 2014 at 4:57 PM, Matthew Taylor <[email protected]> wrote: >> >>> I have more tutorials planned, but I'd like some help deciding which >>> to do first. Please answer this 1-question poll: >>> >>> >>> https://docs.google.com/forms/d/1GBYWg_-LIaYmOz9EJ5LbFo6N2ot1xv9AA22gaNdENs0/viewform?usp=send_form >>> >>> Thanks, >>> --------- >>> Matt Taylor >>> OS Community Flag-Bearer >>> Numenta >>> >>> >> >> ------------------------------ >> >> The material in this message is private and may contain Protected >> Healthcare Information (PHI). If you are not the intended recipient, be >> advised that any unauthorized use, disclosure, copying or the taking of any >> action in reliance on the contents of this information is strictly >> prohibited. If you have received this email in error, please immediately >> notify the sender via telephone or return mail. >> > > >
