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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 | 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 | 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 | cllFzkvrYmE | UCZHmQk67mSJgfCCTn7xBfew | 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 | cllFzkvrYmE | UCZHmQk67mSJgfCCTn7xBfew | 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 | 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 | cllFzkvrYmE | 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 |