Questions tagged [embeddings]
The embeddings tag has no usage guidance.
8 questions
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Facebook's infersent intuition
When reviewing Infersent's architecture here, I noticed that, after encoding the premise and hypothesis to obtain two vectors u and v, they feed the set of fully connected layers with:
(u, v) the ...
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If the curse of dimensionality exists, how does embedding search work?
The curse of dimensionality tells us if the dimension is high, the distance metric will stop working, i.e., everyone will be close to everyone.
However, many machine learning retrieval systems rely on ...
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What is the intuition behind the positional cosine encoding in the transformer network?
I don't understand how adding the cosine encodings/functions to each of the dimension of the word vector embedding enables the network to "understand" where each word is situated in the sentence.
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Embedding data into a larger dimension space
Embeddings or latent spaces are vector spaces that we embed our initial data into that for further processing. The benefit of doing so as far as I am aware, is to reduce the dimension. Often data has ...
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What is the difference between embedding in pure math and embedding in ML?
In ML the term "embedding" gets tossed around a lot and the term basically means the construction of a function that takes a high-dimensional vector to a low-dimensional vector in such a way ...
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Is there an extension of PCA for data embedded in hyperbolic spaces?
I'm working on a project where we are embedding data into an n-dimensional Poincare ball similar to this paper. However, we'd like to take the additional step of reducing this data to a 2-dimensional ...
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Why the positional embeddings for different positions are not confined
for this formulas PE(pos,2i)=sin(pos/(10000^(2i/modelDimension))) and PE(pos,2i+1)=cos(pos/(10000^(2i/modelDimension)))
we know <...
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Embedded markov chain example
I have an example of in my textbook of an "embeded markov chain", where I don't understand one step.
Suppose that $(X_n)_{n\geq 0}$ is Markov$(\lambda, P)$. $\lambda$ is the initial distribution and ...
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