Answers.Body: <p>No; there are too many publications around for anybody to keep track of everything, so unless it is a seminal paper, you cannot draw any conclusions from this. They could simply have missed it.</p>
  
  <p>Especially if it is a textbook for beginners, more advanced papers are often not mentioned, as they might be too complex to understand.</p>
  
  <p>So you have to decide for yourself if that paper is relevant to you. To me it sounds like a specific application of neural networks; if the dimensionality of the input data is an issue for you, it might be, otherwise probably not. </p>
  
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Answers.CreationDate: 2020-04-17T09:28:50.047Z
Answers.LastActivityDate: 2020-04-17T09:28:50.047Z
Answers.UserDisplayName: Oliver Mason
Body: <p>Is the paper "<a href="https://www.cs.toronto.edu/~hinton/science.pdf" rel="nofollow noreferrer">Reducing the Dimensionality of Data with Neural Networks</a>" by G. Hinton and R. Salakhutdinov relevant?</p>
  
  <p>It seems that <a href="https://www.deeplearningbook.org/" rel="nofollow noreferrer">the deep learning textbook by Goodfellow, Bengio &amp; Courville (2016)</a> doesn't cite that paper. </p>
  
  <p>Does that indicate that paper is not as important as others to Deep learning? If yes, I would skip this one to accelerate my process of learning.</p>
  
Tags: <research><papers>
Title: Is the paper "Reducing the Dimensionality of Data with Neural Networks" by Hinton relevant?
UserDisplayName: Piete3r
ThreadText: # Is the paper "Reducing the Dimensionality of Data with Neural Networks" by Hinton relevant?
  
  (Asked by: username_0 on 2020-04-17)
  
  Is the paper "[Reducing the Dimensionality of Data with Neural Networks](https://www.cs.toronto.edu/~hinton/science.pdf)" by G. Hinton and R. Salakhutdinov relevant?
  
  It seems that [the deep learning textbook by Goodfellow, Bengio & Courville (2016)](https://www.deeplearningbook.org/) doesn't cite that paper.
  
  Does that indicate that paper is not as important as others to Deep learning? If yes, I would skip this one to accelerate my process of learning.
  
  **Tags:** research, papers
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-04-17. Score: 1
  
  No; there are too many publications around for anybody to keep track of everything, so unless it is a seminal paper, you cannot draw any conclusions from this. They could simply have missed it.
  
  Especially if it is a textbook for beginners, more advanced papers are often not mentioned, as they might be too complex to understand.
  
  So you have to decide for yourself if that paper is relevant to you. To me it sounds like a specific application of neural networks; if the dimensionality of the input data is an issue for you, it might be, otherwise probably not.
  
  
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