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GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3005.7400000000002
|
any old information might come in, and we might collapse and or we might never reach
| 3,005.74 | 3,016.34 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
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cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3010.8
|
consensus because any old information might come in. However, if we introduce the attention
| 3,010.8 | 3,022.12 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3016.34
|
mechanism into this whole thing, and only draw in information from the selected neighbors
| 3,016.34 | 3,028.68 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3022.1200000000003
|
that already are in the same group in the same island as me, then this consensus algorithm
| 3,022.12 | 3,034.44 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3028.6800000000003
|
works. So if the network, the network is now forced kind of to learn to build these islands
| 3,028.68 | 3,042.6 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3034.44
|
of similar things in order to make this consensus work if we regularize this consensus. So I
| 3,034.44 | 3,049.92 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3042.6
|
believe he makes the case for the attention mechanism. I don't think he, in this case,
| 3,042.6 | 3,056.88 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3049.92
|
considers kind of the up the next up layer islands, what I would say is you need to consider
| 3,049.92 | 3,066.36 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3056.88
|
the island membership all the way up the columns in order to decide which things which locations,
| 3,056.88 | 3,073 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3066.36
|
right, it's free to choose which embeddings at other locations it should resemble. I think,
| 3,066.36 | 3,084.2 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3073.0
|
yeah, this is the case for the attention mechanism. Okay, I hope you're still half with me. If
| 3,073 | 3,090.66 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3084.2
|
not, I'm, I'm bit confused too. But I think what he's doing is he says, contrastive learning
| 3,084.2 | 3,096.24 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3090.66
|
would be good, you can use it, but you have to be careful at which layer you do it. Another
| 3,090.66 | 3,104.72 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3096.24
|
regularizer to form these islands would be this regularize the network to conform to
| 3,096.24 | 3,110.96 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3104.72
|
the consensus option, opinion. However, if you simply aggregate information from the
| 3,104.72 | 3,118.04 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3110.96
|
same layer, then that wouldn't work because, you know, the different things in the same
| 3,110.96 | 3,123.6 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3118.04
|
layer might correspond to completely different parts of the image. Drawing in information
| 3,118.04 | 3,128.52 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3123.6
|
from there would not help you. How do you solve this by introducing the very attention
| 3,123.6 | 3,134.98 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3128.52
|
mechanism that he introduced in order to only draw in information from parts of the same
| 3,128.52 | 3,144.8 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3134.98
|
layer that actually are related to you? Okay, the next thing, the next consideration he
| 3,134.98 | 3,151.06 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3144.8
|
does is representing coordinate transformations. How does this represent coordinate transformations,
| 3,144.8 | 3,158.14 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3151.06
|
there was a capsule net paper where he explicitly represents coordinate transformations in kind
| 3,151.06 | 3,166.32 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3158.14
|
of four dimension quaternion space. And he says, that is probably not needed, because
| 3,158.14 | 3,176.92 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3166.3199999999997
|
you don't want to hear says you could represent this by a by four by four matrices. However,
| 3,166.32 | 3,182.68 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3176.92
|
if you simply allocate 16 numbers in each embedding vector, in order to represent the
| 3,176.92 | 3,187.04 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3182.68
|
part whole coordinate transformation, like the transformation that relates the part to
| 3,182.68 | 3,192.52 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3187.04
|
the whole, that does not make it easy to represent uncertainty about the aspects of pose and
| 3,187.04 | 3,198.8 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3192.52
|
certainty about others. So the problem here is that we know that humans, when they watch
| 3,192.52 | 3,206.4 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3198.8
|
something right here, when they watch a scene, like this is a chair, and there is a person,
| 3,198.8 | 3,212.44 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3206.4
|
a very tiny person on the chair, we don't see necessarily the coordinate frame of the
| 3,206.4 | 3,217.52 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3212.44
|
world, what we see is we see the coordinate frame of the chair, like maybe this is the
| 3,212.44 | 3,225.16 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3217.52
|
center, and we see the person in relation to the chair, our brain seems to do this intuitively,
| 3,217.52 | 3,229.98 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3225.16
|
and hinting things that a system like this should also do it intuitively. So somehow,
| 3,225.16 | 3,234.84 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3229.98
|
the coordinate transformations involved going from the eye to the reference through the
| 3,229.98 | 3,241.72 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3234.84
|
frame of the chair, and then from the chair to the person, they should be somehow in encoded
| 3,234.84 | 3,248.32 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3241.72
|
in this network. However, he also says that it's probably not necessary to encode them
| 3,241.72 | 3,252.84 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3248.3199999999997
|
explicitly as you know, explicit coordinate transformations, because not only does that
| 3,248.32 | 3,259.8 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3252.8399999999997
|
make it harder, probably to learn, but also, you can't represent uncertainty. In fact,
| 3,252.84 | 3,264.88 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3259.7999999999997
|
you can represent uncertainty, that's the next thing right here, much better by having
| 3,259.8 | 3,272.24 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3264.88
|
a higher dimensional thing that you're trying to guess, right? If you are trying to guess
| 3,264.88 | 3,277.76 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3272.2400000000002
|
a distribution with three components, and you simply have a three dimensional vector,
| 3,272.24 | 3,283.5 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3277.76
|
you have no way of representing uncertainty. However, if you have a nine dimensional vector,
| 3,277.76 | 3,290.62 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
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cllFzkvrYmE-t3283.5
|
you can have three opinions about the distribution. So this is an opinion, this is an opinion,
| 3,283.5 | 3,295.88 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3290.62
|
and then this is an opinion. And then you can sort of aggregate and you can say, Well,
| 3,290.62 | 3,301.12 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
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cllFzkvrYmE
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UCZHmQk67mSJgfCCTn7xBfew
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cllFzkvrYmE-t3295.88
|
I'm pretty sure about these two things, because all my opinions are pretty close. But this
| 3,295.88 | 3,309.32 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
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cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
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cllFzkvrYmE-t3301.12
|
one here, I'm not so sure because my individual things say different things, things say things.
| 3,301.12 | 3,315.48 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3309.3199999999997
|
All right, I've this video is too long. So that's his argument right here, we don't need
| 3,309.32 | 3,322.78 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
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cllFzkvrYmE-t3315.48
|
explicit representing of uncertainty, because by simply over parameterizing, we can already
| 3,315.48 | 3,331.98 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3322.78
|
represent uncertainty well. And we also don't need disentangled position information and,
| 3,322.78 | 3,341.52 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3331.98
|
and so on. Sorry, we don't need different position informations, because, again, the
| 3,331.98 | 3,346.88 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3341.52
|
work can take care of that. And he gives a good example, like why would you have disentangled
| 3,341.52 | 3,357.48 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
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cllFzkvrYmE
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UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3346.88
|
coordinate frame if you have an image? And in the image, the picture in it is this. How
| 3,346.88 | 3,367.12 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3357.48
|
do you know if that is a rhomboid shape? Or if it is a rec, if it is a rectangular piece
| 3,357.48 | 3,373.64 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3367.12
|
of paper viewed from the side, I should probably draw it way closer, something like something
| 3,367.12 | 3,382.08 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3373.64
|
like this. I suck at this. You get probably get what I mean. Like, if it is a different
| 3,373.64 | 3,389.4 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3382.08
|
object, it has a like the object and the coordinate transformation are dependent upon each other.
| 3,382.08 | 3,394.9 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3389.4
|
And so it makes sense for the neural network to actually entangle the two, because the
| 3,389.4 | 3,401.52 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3394.9
|
two things depend on each other. In essence, he's just saying, don't worry about explicitly
| 3,394.9 | 3,407.72 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3401.52
|
representing all of the different things. We got it like the neural network can do all
| 3,401.52 | 3,415.12 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3407.7200000000003
|
of these things, like uncertainty or position, and post transformations. So here he compares
| 3,407.72 | 3,425.4 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3415.12
|
it to different other architectures. comparison to CNN comparison to transformers comparison
| 3,415.12 | 3,430.88 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3425.4
|
to capsule models. And at the end, it goes into video at the very beginning, he says,
| 3,425.4 | 3,437.48 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3430.88
|
the paper is about actually a video system. And you can kind of see that because we go
| 3,430.88 | 3,443.98 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3437.48
|
through this algorithm in multiple time steps, right? You have, it's like you analyze an
| 3,437.48 | 3,451.16 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3443.98
|
image with these columns, which gives you sort of a 3d 3d tensor with the image at the
| 3,443.98 | 3,458.04 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3451.16
|
bottom. And you go in the next time step, you have a new 3d tensor, right, you pass
| 3,451.16 | 3,464.76 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3458.04
|
this whole information around with the image at the bottom. And it says, well, why does
| 3,458.04 | 3,469.3 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3464.76
|
that need to be the same image that could also be different images. So you could use
| 3,464.76 | 3,476.26 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3469.3
|
the system to analyze video. So what he does is he says, at the same time, you do this
| 3,469.3 | 3,482.58 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3476.26
|
time step to find agreement, you could actually swap out the video frame, the X, you can swap
| 3,476.26 | 3,487.12 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3482.5800000000004
|
out the video frame, and produce a slightly different video frame. And you could actually
| 3,482.58 | 3,493.24 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3487.1200000000003
|
have a kind of an ensemble regularizing effect. So as the whole columns here, the whole system
| 3,487.12 | 3,499.72 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3493.24
|
comes to a consensus over time, you feed in different information at the bottom. And what
| 3,493.24 | 3,507.28 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3499.72
|
he says is that, you know, if this is a slow enough video, then the top layers here would
| 3,499.72 | 3,513.28 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3507.2799999999997
|
probably could still reach an agreement, while the bottom layers would change rapidly. But
| 3,507.28 | 3,521 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3513.2799999999997
|
that could be sort of an ensemble or a regularizer, regularizing effect that it even has. So he
| 3,513.28 | 3,526.48 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3521.0
|
intrinsically connects these two time dimensions, because they would be separate, right, you
| 3,521 | 3,533.64 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3526.48
|
could input a video. And then in, you know, in each frame, you could do this consensus
| 3,526.48 | 3,539.32 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3533.64
|
finding algorithm. But he says, No, it's actually cool to consider them together to do the consensus
| 3,533.64 | 3,545.24 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3539.32
|
finding while you sort of watch the video, it's just not clear that you always need the
| 3,539.32 | 3,550.8 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3545.24
|
same amount of consensus finding steps as you need as you have video frames. So maybe,
| 3,545.24 | 3,556 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3550.7999999999997
|
you want to, maybe you want to take like five consensus steps per video frame, or the other
| 3,550.8 | 3,564.16 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3556.0
|
way around? Not sure. In any case, I think that's a pretty cool idea. And he says things
| 3,556 | 3,569.64 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3564.16
|
like, if the changes are rapid, there is no time available to iteratively settle on a
| 3,564.16 | 3,574.64 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3569.64
|
good set of embedding vectors for interpreting a specific frame. This means that the glom
| 3,569.64 | 3,580.56 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3574.64
|
architecture cannot correctly interpret complicated shapes. If the images are changing rapidly,
| 3,574.64 | 3,585.24 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3580.56
|
try taking an irregularly shaped potato and throwing it up in the air such a way that
| 3,580.56 | 3,590.76 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3585.24
|
it rotates at one or two cycles per second. Even if you smoothly track the potato, you
| 3,585.24 | 3,596.88 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3590.7599999999998
|
cannot see what shape it is. Now I don't have a potato, but I can give you an avocado. So
| 3,590.76 | 3,613.2 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3596.88
|
if you give me a second, how's that? Could you track the shape? I don't know. Probably
| 3,596.88 | 3,621.8 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3613.2000000000003
|
in his correct. All right, he talks about is this biologically plausible? And I don't
| 3,613.2 | 3,626.6 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3621.8
|
want to go too much into this. He discusses some restrictions like yeah, we still use
| 3,621.8 | 3,632.4 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3626.6
|
backprop and is backprop plausible and so on. I love this sentence. In the long run,
| 3,626.6 | 3,638.68 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3632.4
|
however, we are all dead. And then the footnote saying there are alternative facts. But yeah,
| 3,632.4 | 3,645.48 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3638.68
|
he discusses whether it's biological plausible. How could you modify it to make it more plausible?
| 3,638.68 | 3,652.64 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3645.48
|
For example, when you want to do contrastive learning, there is evidence that dreams during
| 3,645.48 | 3,657.32 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3652.64
|
so during sleep, you do contrastive learning, like you produce the negative examples during
| 3,652.64 | 3,665 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3657.3199999999997
|
sleep, and then during the day, you collect the positive examples and so on. So I think
| 3,657.32 | 3,673.08 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3665.0
|
this is a more speculative part of the paper, but it's pretty cool to it's pretty cool to
| 3,665 | 3,680.28 |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
|
2021-02-27 15:47:03
|
https://youtu.be/cllFzkvrYmE
|
cllFzkvrYmE
|
UCZHmQk67mSJgfCCTn7xBfew
|
cllFzkvrYmE-t3673.08
|
read it. And lastly, he goes into discussion. He also says that this paper is too long already.
| 3,673.08 | 3,686.44 |
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