Quinn, the older HTM implementations were completely different algorithms and are now obsolete.
On Mon, Jul 15, 2013 at 1:09 PM, Quinn Liu <[email protected]> wrote: > Hi Michael, > I had an additional question. In your reply you remarked that "while > digit recognition was successfully modeled with the original version of > HTM, that doesn't seem to be the case with CLA yet". I was wondering if you > or anyone else could expand on this as I am unfamiliar with the original > version of the HTM. Assuming that it is premature version of the current > spatial and temporal learning algorithms how is it different? Thanks! > > Best Regards, > Quinn Liu > > [email protected] > > > On Mon, Jul 15, 2013 at 3:41 PM, Michael Ferrier < > [email protected]> wrote: > >> Hi Fergal, >> >> I completely agree that a visual object recognition system would greatly >> benefit from hierarchy. Causes in the world are hierarchical, and the brain >> uses hierarchy to learn and represent them. The successful vision models >> using the original implementation of HTM were also hierarchical. I was just >> saying that, as far as I know, this hasn't been done with CLA yet -- >> according to Jeff, in their vision experiments they were just beginning to >> expand beyond one layer when they stopped working on vision. >> >> I think that both temporal pooling (for invariance) and hierarchy are key >> to using CLA for visual recognition problems, but I don't know of anyone >> who has put all the pieces together yet to do visual recognition with CLA. >> >> -Mike >> >> >> >> _____________ >> Michael Ferrier >> Department of Cognitive, Linguistic and Psychological Sciences, Brown >> University >> [email protected] >> >> >> On Mon, Jul 15, 2013 at 11:44 AM, Fergal Byrne < >> [email protected]> wrote: >> >>> >>> Hi Michael, >>> >>> Handwritten characters are undoubtedly multi-component designs, which >>> have evolved to connect with and trigger our ability to learn spatial, >>> temporal and hierarchical patterns. We perceive the same characters even >>> when loads of things change in fonts, and especially when reading different >>> people's handwriting. We can fill in gaps and correct misspellings. So the >>> learning and prediction must be several levels deep in hierarchy. >>> >>> In terms of bottom level mechanics, we use saccades to recognise and >>> "delocalise" components such as characters, facial features, etc, in such a >>> way as to allow this multi-level recognition (including a hierarchy of >>> fixations - for strokes, junctions, topology, characters, letters, words, >>> and even sentences). >>> >>> Speed-readers can saccade to read entire phrases and sentences at a >>> time, allowing reading speeds of thousands of words per minute with better >>> than 70% comprehension scores. With practice, I've been able to get scores >>> in the 1-2000 wpm range. I can also read text in a mirror or upside-down at >>> speeds approaching 50-60% of an average reader. These things could only be >>> done using big, complex region hierarchies with vast volumes of (normal) >>> reading practice. >>> >>> I would have predicted that a single layer CLA would struggle with >>> this kind of data set, because it lacks the multi-level upward and downward >>> structure which I feel this kind of performance requires. >>> >>> Regards, >>> >>> Fergal Byrne >>> >>> _______________________________________________ >>> nupic mailing list >>> [email protected] >>> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >>> >>> >> >> _______________________________________________ >> nupic mailing list >> [email protected] >> http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >> >> > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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