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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-t2372.92 | color. So what he's saying right here is that, you know, you could give preference to two | 2,372.92 | 2,387.84 |
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-t2380.72 | similar color locations when you decide what you want to attend to. But the color isn't | 2,380.72 | 2,394.76 |
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-t2387.8399999999997 | as easy as simply saying what color is there in the location that you are at. But you could | 2,387.84 | 2,403.36 |
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-t2394.76 | be so if this is green, and this here is blue, then the bottom layer would say yes, I'm green | 2,394.76 | 2,409 |
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-t2403.36 | and yes, I'm blue. But they could also be saying, well, I am part of a green blue object, | 2,403.36 | 2,415.76 |
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-t2409.0 | right? And then the the higher layer here, you know, attending or caring about multiple | 2,409 | 2,421.18 |
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-t2415.76 | or bigger region, its color would then be, you know, green, blue, and the consensus could | 2,415.76 | 2,427.16 |
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-t2421.18 | reach on, well, we are a green blue object, even though the object isn't a pure green | 2,421.18 | 2,436 |
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-t2427.16 | or pure blue all throughout. So he, I think, yeah, it's it's, I think it's a side suggestion. | 2,427.16 | 2,443 |
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-t2436.0 | Maybe he has this as a core motivation between the system. But it's just interesting to see | 2,436 | 2,450.6 |
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-t2443.0 | how he thinks of things and he extends the color here to textures and even shapes. The | 2,443 | 2,455.04 |
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-t2450.6 | individual texture elements have their own shapes and poses in spatial relationships, | 2,450.6 | 2,459.2 |
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-t2455.04 | but an object with a textured surface has exactly the same texture everywhere at the | 2,455.04 | 2,466.4 |
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-t2459.2 | object level. glom extends this ideas to shapes, an object may have parts that are very different | 2,459.2 | 2,471.18 |
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-t2466.4 | from one another. But at the object level, it has exactly the same compound shape in | 2,466.4 | 2,476.28 |
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-t2471.18 | all of the location that it occupies, basically saying that, okay, every pixel that's part | 2,471.18 | 2,481.72 |
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-t2476.28 | of a cat head is a is a cat head has the shape of a cat head, even though the individual | 2,476.28 | 2,487.66 |
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-t2481.7200000000003 | locations might not recognize that. And that information could be passed around through | 2,481.72 | 2,495.16 |
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-t2487.6600000000003 | this consensus mechanism over time. So the cluster discovery versus cluster formation, | 2,487.66 | 2,502.04 |
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-t2495.1600000000003 | we've seen that and he makes a lot of, he makes a lot of analogies to face recognition. | 2,495.16 | 2,507.16 |
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-t2502.04 | But yeah, the clusters are not the islands of similar embedding vectors at a level can | 2,502.04 | 2,512.1 |
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-t2507.16 | be viewed as clusters, but these clusters are not discovered in immutable data. They | 2,507.16 | 2,517.82 |
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-t2512.1 | are formed by the interaction between the intra level process that favors islands of | 2,512.1 | 2,523.4 |
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-t2517.82 | similarity and dynamically changing suggestions coming from the locations embedding at adjacent | 2,517.82 | 2,531.88 |
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-t2523.4 | levels. So the core here is really this consensus algorithm that creates these clusters. And | 2,523.4 | 2,535.72 |
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-t2531.88 | yeah, the clustering algorithm doesn't work by simply looking at embeddings and deciding | 2,531.88 | 2,540.68 |
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-t2535.7200000000003 | which ones go together, but the embeddings themselves update themselves in order to form | 2,535.72 | 2,548.76 |
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-t2540.6800000000003 | clusters. And yeah, this is a replicating embedding vectors. This is a response to a | 2,540.68 | 2,555.24 |
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-t2548.76 | criticism that I guess he got where someone said, Well, why don't Why do you represent | 2,548.76 | 2,559.12 |
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-t2555.2400000000002 | if you have these, you know, these columns at the bottom, it makes sense, you have all | 2,555.24 | 2,562.84 |
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-t2559.12 | the different vectors, but then as you go up, you know, you have that kind of the same | 2,559.12 | 2,568.6 |
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-t2562.8399999999997 | vector for all locations, because it's the same object, why does it make sense to replicate | 2,562.84 | 2,575.88 |
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-t2568.6 | that everywhere, and not just have one, because, you know, in a database, we just have one. | 2,568.6 | 2,581.12 |
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-t2575.88 | And it basically says that, in order to reach the consensus, first of all, it's important | 2,575.88 | 2,585.4 |
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-t2581.12 | to have different vectors, they might be slightly different. So they might have some nuance | 2,581.12 | 2,590.56 |
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-t2585.4 | in them, because, you know, they might get pulled into different directions from the | 2,585.4 | 2,597.6 |
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-t2590.56 | sign of bottom up signal, then from the consensus algorithm on the same layer. So I, you know, | 2,590.56 | 2,603.84 |
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-t2597.6 | I believe that it is that is important. Here, I think it's just this is a criticism he got. | 2,597.6 | 2,611 |
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-t2603.84 | And then he decided to put this in here, learning islands. So what we haven't discussed about | 2,603.84 | 2,618.48 |
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-t2611.0 | this yet is how this is trained. And Hinton says this is trained as a denoising auto encoder. | 2,611 | 2,623.98 |
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-t2618.48 | Let us assume that glom is trained to reconstruct at its output, the uncorrupted version of | 2,618.48 | 2,632.2 |
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-t2623.98 | an image from which some region has been have been removed. So he goes into self supervised | 2,623.98 | 2,638.36 |
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-t2632.2 | learning with this system. This objective should ensure that information about the input | 2,632.2 | 2,643.52 |
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-t2638.36 | is preserved during the forward pass. And if the regions are sufficiently large, it | 2,638.36 | 2,648.48 |
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-t2643.52 | should also ensure that identifying familiar objects will be helpful for filling in the | 2,643.52 | 2,656.84 |
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-t2648.48 | missing regions. To encourage islands of near identity, we need to add a regularizer. And | 2,648.48 | 2,660.88 |
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-t2656.84 | experience shows that a regularizer that simply encourages similarity between the embeddings | 2,656.84 | 2,667.28 |
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-t2660.88 | of nearby locations can cause representations to collapse. All the embedding vectors may | 2,660.88 | 2,672.32 |
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-t2667.28 | become very small, so that they are all very similar. And the reconstruction will then | 2,667.28 | 2,676.92 |
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-t2672.32 | use very large weights to deal with the very small scale to prevent collapse. And then | 2,672.32 | 2,683.84 |
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-t2676.92 | he says contrastive learning is the answer to this. So how do you regularize the model | 2,676.92 | 2,691.92 |
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-t2683.84 | such that this consensus is formed? He says contrastive learning might be useful, but | 2,683.84 | 2,698.02 |
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-t2691.92 | you can't simply apply it straight out. So it learns to make representations of two different | 2,691.92 | 2,703.18 |
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-t2698.02 | crops of the same image agree, and the representations of two crops from different images disagree. | 2,698.02 | 2,709.24 |
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-t2703.1800000000003 | But this is not a sensible thing to do. If our aim is to recognize objects, if crop one | 2,703.18 | 2,714.96 |
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-t2709.2400000000002 | contains objects A and B and crop two from the same image contains objects B and C, it | 2,709.24 | 2,720.34 |
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-t2714.96 | does not make sense to demand that the representation of the two crops is the same at the object | 2,714.96 | 2,727.56 |
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-t2720.34 | level. Okay, so he says that contrastive learning is good, but you have to pay very careful | 2,720.34 | 2,736.88 |
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-t2727.56 | attention at which layer you employ it. Because, you know, if you go down far enough, then | 2,727.56 | 2,742.36 |
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-t2736.88 | contrastive learning, especially, you know, this this type where you crop the image into | 2,736.88 | 2,746.28 |
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-t2742.36 | different parts, and you say, well, since it's the same image, the representations should | 2,742.36 | 2,751.48 |
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-t2746.28 | agree in would say, well, at the top layer, yes, but at the bottom layer, certainly not | 2,746.28 | 2,759.72 |
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-t2751.48 | because they display different things, right. So you have to be careful where you apply | 2,751.48 | 2,767.4 |
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-t2759.7200000000003 | this contrastive learning. And he gives a bunch of suggestions on how to solve that. | 2,759.72 | 2,773.12 |
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-t2767.4 | He says things like, well, negative examples, for example, might not might not even be needed. | 2,767.4 | 2,778.84 |
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-t2773.12 | Well, that's it. Sorry, that's a different thing. So the obvious solution is to regularize | 2,773.12 | 2,783.16 |
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-t2778.8399999999997 | the bottom up and top down neural networks by encouraging each of them to predict the | 2,778.84 | 2,793.24 |
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-t2783.16 | consensus option. Yeah, this is the way to geometric mean of the predictions coming from | 2,783.16 | 2,798.16 |
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-t2793.24 | the top down and bottom up networks, the attention weighted average of the embeddings at nearby | 2,793.24 | 2,804.6 |
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-t2798.16 | locations at the previous time step, the previous state of end, I guess, and there should be | 2,798.16 | 2,810.92 |
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-t2804.6 | an end, and the previous state of the embedding, training the inter level prediction to agree | 2,804.6 | 2,815.52 |
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-t2810.92 | with the consensus will clearly make the islands found during feed forward inference be more | 2,810.92 | 2,825.74 |
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-t2815.52 | coherent. So he says you could regularize the model to, to regress to the consensus | 2,815.52 | 2,833.96 |
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-t2825.74 | option. So it's sort of like a self a self regression. And he asks whether or not that | 2,825.74 | 2,840.9 |
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-t2833.9599999999996 | will lead to a collapse. Because if you don't have negative examples and contrastive learning, | 2,833.96 | 2,848.24 |
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-t2840.8999999999996 | this could lead to simply a collapse. An important question is whether this type of training | 2,840.9 | 2,853.08 |
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-t2848.24 | will necessarily cause collapse if it is not accompanied by training the inter level predictions | 2,848.24 | 2,858.4 |
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-t2853.08 | to be different for negative examples that use the consensus options for unrelated spatial | 2,853.08 | 2,865.84 |
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-t2858.4 | contexts. So here is that problem, right? If you use the consensus opinion for unrelated | 2,858.4 | 2,875.32 |
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-t2865.84 | spatial context, that might be a problem. He says using layer or batch norm should reduce | 2,865.84 | 2,880.24 |
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-t2875.3199999999997 | the tendency to collapse, but a more important consideration may be the achievability of | 2,875.32 | 2,888.9 |
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-t2880.24 | the goal. It goes into why regularization could help. And he says, if however, an embedding | 2,880.24 | 2,893.84 |
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-t2888.8999999999996 | at one location is free to choose which embeddings at other locations, it should resemble, the | 2,888.9 | 2,898.52 |
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-t2893.8399999999997 | goal can be achieved almost perfectly by learning to form islands of identical vectors and attending | 2,893.84 | 2,905.4 |
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-t2898.52 | almost entirely to other locations that are in the same island. And I don't know, I don't | 2,898.52 | 2,913.4 |
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-t2905.4 | know if this is what I suggested. So I guess this is kind of a convoluted paragraph. And | 2,905.4 | 2,918.76 |
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-t2913.4 | I had to also read it multiple times. And I still don't exactly know what he's trying | 2,913.4 | 2,925.8 |
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-t2918.76 | to say right here. But I think what he's saying is that what we want to do is we want to sort | 2,918.76 | 2,933.38 |
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-t2925.8 | of regularize the network to produce this consensus, right? So we have a bottom up signal, | 2,925.8 | 2,939.08 |
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-t2933.38 | a top down signal, we have a current value, and we have the signal from the attention | 2,933.38 | 2,945.8 |
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-t2939.08 | mechanism. Now, what we want to do is we want to reach a consensus such that these islands | 2,939.08 | 2,953.46 |
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-t2945.8 | form. However, if you attend to any sort of things here that have nothing to do with you, | 2,945.8 | 2,959.1 |
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-t2953.46 | you might not be able to reach this consensus, right? That's I think that's the problem I | 2,953.46 | 2,966.2 |
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-t2959.1 | think he's touching on the problem that I said before. So what he says is, you know, | 2,959.1 | 2,974.44 |
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-t2966.2 | what you should do is you should simply attend to things that are in the same islands already. | 2,966.2 | 2,979.12 |
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-t2974.44 | So if an embedding at one location is free to choose which embedding at other locations, | 2,974.44 | 2,985.28 |
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-t2979.12 | it should resemble the goal can be achieved by learning to form islands of identical vectors | 2,979.12 | 2,992.08 |
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-t2985.28 | and attending almost entirely to other locations that are in the same island. Now, I think | 2,985.28 | 2,998.76 |
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-t2992.0800000000004 | here, what he's doing, he makes the case for the attention mechanism itself, right? So | 2,992.08 | 3,005.74 |
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-t2998.76 | he says, if if we simply draw in information from the same layer here, you know, anything, | 2,998.76 | 3,010.8 |