Answers.Body: <p>I think any kind of <a href="https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems)" rel="nofollow noreferrer">matrix factorization</a> technique would do the trick.
  You can find a lot of <a href="https://cla2019.github.io/embedmatrix.pdf" rel="nofollow noreferrer">resources</a> related to natural language processing about it. Of course everything is applicable outside the NLP realm.</p>
  
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Answers.CreationDate: 2022-12-03T15:59:51.463Z
Answers.Text: Thank you for your reply, I will look into the resources you provided. My idea is indeed that I want to apply this to NLP. So let's say I want to project whole sentences into the latent space through matrix factorization, do small, linear movements in that latent space always lead to a meaningful result?
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Answers.CreationDate: 2022-12-03T16:09:36.803Z
Answers.Text: I am asking because I want the latent space to be differentiable.
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Answers.CreationDate: 2022-12-02T13:36:32.667Z
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Answers.UserDisplayName: Edoardo Guerriero
Body: <p>Let's say I have a discrete input and want to transform it into a differentiable latent space. What models/algorithms besides variational autoencoders can I use?</p>
  
Tags: <natural-language-processing><algorithm-request><variational-autoencoder><model-request>
Title: What models/algorithms besides variational autoencoders can I use to transform a discrete input into a differentiable latent space?
UserDisplayName: postnubilaphoebus
ThreadText: # What models/algorithms besides variational autoencoders can I use to transform a discrete input into a differentiable latent space?
  
  (Asked by: username_0 on 2022-12-02)
  
  Let's say I have a discrete input and want to transform it into a differentiable latent space. What models/algorithms besides variational autoencoders can I use?
  
  **Tags:** natural-language-processing, algorithm-request, variational-autoencoder, model-request
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-12-02. Score: 1
  
  I think any kind of [matrix factorization](https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems)) technique would do the trick.
  You can find a lot of [resources](https://cla2019.github.io/embedmatrix.pdf) related to natural language processing about it. Of course everything is applicable outside the NLP realm.
  
  > **Comments:**
  > **username_0**: Thank you for your reply, I will look into the resources you provided. My idea is indeed that I want to apply this to NLP. So let's say I want to project whole sentences into the latent space through matrix factorization, do small, linear movements in that latent space always lead to a meaningful result?  
  > **username_0**: I am asking because I want the latent space to be differentiable.  
  
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