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> Natural data is "generated" from a constrained hierarchical / compositional function. > Deep networks learns that hidden structure from polynomially few examples and creatively generate exponentially many valid new ones. > The depth of the network is what's important to overcome the curse of dimensionality, and potentially invalidate...

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Machine Learning Street Talk

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13,826 просмотров • 4 месяцев назад •via X (Twitter)

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Фото профиля Machine Learning Street Talk
Machine Learning Street Talk4 месяцев назад

> For data with hidden hierarchy, the data gap is exponentially large. > How could this be better? - models that predict their own abstractions/latents. > Naturally, @ylecun agrees!

Фото профиля Machine Learning Street Talk
Machine Learning Street Talk4 месяцев назад

> Generic high-dimensional data is unlearnable, you need more data than atoms in the universe just to interpolate. > The way out of this is coarse graining, which is often exemplified by word2vec i.e. the concept of a "street" emerges from locality statistics. > But the most abstract concepts are the hardest to learn.

Фото профиля Matthieu wyart
Matthieu wyart4 месяцев назад

Great fun, Tim. The part I'd underline: the same compositional structure that makes data learnable from polynomially few examples is what lets a model recombine them into exponentially many new valid ones: creativity and sample-efficiency are two sides of one coin.

Фото профиля Lonely nerd.
Lonely nerd.4 месяцев назад

The one take away from the post AI hype is that LLMs and most researchers are interpolators. Very impressive ones. =-) 👇

Фото профиля Warley Vital
Warley Vital4 месяцев назад

Interestingly, the previous post in my timeline was this one that points to a similar idea

Фото профиля Mike Pearson
Mike Pearson4 месяцев назад

Processes can't be represented by single passes through a network. They're picking up the outputs of processes - which do have some stable patterns. Like memorizing times tables as opposed to learning multiplication as an algorithm. Recursion is required.

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