
Feature extraction without learning using Hierarchical Temporal Memory - godelmachine
https://arxiv.org/abs/1803.05131
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godelmachine
Abstract →

Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates
sparsity, hierarchy and modularity resembling the working principles of
neocortex. Feature encoding is an important step to create sparse binary
patterns. This sparsity is introduced by the binary weights and random weight
assignment in the initialization stage of the HTM. We propose the alternative
deterministic method for the HTM initialization stage, which connects the HTM
weights to the input data and preserves natural sparsity of the input
information. Further, we introduce the hardware implementation of the
deterministic approach and compare it to the traditional HTM and existing
hardware implementation. We test the proposed approach on the face recognition
problem and show that it outperforms the conventional HTM approach.

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p1esk
It's not hard to outperform the "conventional HTM approach", because it does
not work (yet). They tested face recognition performance on 3 very old
datasets (AR, ORL, and Yale), and reported accuracy of 83%-86%. To provide
some perspective, these datasets have been solved at least a decade ago (> 99%
accuracy) [1]

But wait, have they built some cool analog hardware with memristors? Nope,
just SPICE simulations...

So yeah, from Kazakhstan, the land of Borat, with love! (sorry, couldn't
resist).

[1]
[http://www.coxlab.org/pdfs/ECCV_PintoDiCarloCox_2008.pdf](http://www.coxlab.org/pdfs/ECCV_PintoDiCarloCox_2008.pdf)

~~~
txsh
I’m surprised to see such blatant racism on Hacker News.

