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
>>
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>
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