Answers.Body: <p>It is already combined.</p>
  
  <p>Adaptive entropy techniques are already used in most of the best compression encoders.  This is true for file encoders, video encoders, and audio encoders.  We use it in the solar lab to optimize sample rates in data acquisition.</p>
  
  <p>In fact, pattern recognition and compression are very tightly coupled if you consider autoencoders and other feature extraction schemes and compare them mathematically with what compression does.  See <a href="https://link.springer.com/chapter/10.1007/978-3-642-10226-4_16" rel="nofollow noreferrer"><em>Data Compression &ndash; A Generic Principle of Pattern Recognition?</em>, Gunther Heidemann, Helge Ritter, VISIGRAPP 2008</a></p>
  
  <p>Zlib and lz4 have more like hyper-parametric learning, however they don't persist what they learn.  This work is interesting: <a href="https://www.cs.ucsb.edu/~ckrintz/papers/acejournal.pdf" rel="nofollow noreferrer"><em>Adaptive On-the-Fly Compression</em>, Chandra Krintz, Sezgin Sucu, IEEE Parallel and Distributed Systems, v17 n1, January 2006</a>.</p>
  
  <p>**Suggested Project: </p>
  
  <p>Create a theoretical framework and software POC that learns correlations between these two sets.</p>
  
  <ol>
  <li>Quickly ascertainable features of documents or audio or video streams (i.e file path components, media titles, date, file type, and first N bytes)</li>
  <li>The existing parameters that existing open source compression software learns during its existing pattern recognition algorithms</li>
  </ol>
  
  <p>Persisting those correlations between compression invocations may considerably improve file transfer, kernel operations (since lz4 is now native in kernels like LINUX), and media streaming.</p>
  
  <p>How much effort is made to persist features extracted (pattern recognition) between frames in media streaming is worth investigating too.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-07-30T22:40:26.090Z
Answers.LastActivityDate: 2018-07-30T22:40:26.090Z
Answers.UserDisplayName: Douglas Daseeco
Answers.Body: <p>Recurrent neural networks can be trained on character level data to generate sentences which are very similar to human language. Go through this <a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow noreferrer">link</a>. You could experiment with them for compressing text.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-05-31T17:24:11.020Z
Answers.LastActivityDate: 2018-07-31T01:25:06.713Z
Answers.LastEditDate: 2018-07-31T01:25:06.713Z
Answers.UserDisplayName: thecomplexitytheorist
Body: <p>What is the current research in artificial intelligence and machine learning in the field of data compression?</p>
  <p>I have done my research on the <a href="https://en.wikipedia.org/wiki/PAQ" rel="nofollow noreferrer">PAQ series of compressors</a>, some of which use neural networks for context mixing.</p>
  
Tags: <neural-networks><machine-learning><reference-request><state-of-the-art><data-compression>
Title: What is the current research in artificial intelligence in the field of data compression?
UserDisplayName: user15736
ThreadText: # What is the current research in artificial intelligence in the field of data compression?
  
  (Asked by: username_0 on 2018-05-19)
  
  What is the current research in artificial intelligence and machine learning in the field of data compression?
  
  I have done my research on the [PAQ series of compressors](https://en.wikipedia.org/wiki/PAQ), some of which use neural networks for context mixing.
  
  **Tags:** neural-networks, machine-learning, reference-request, state-of-the-art, data-compression
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2018-07-30. Score: 2
  
  It is already combined.
  
  Adaptive entropy techniques are already used in most of the best compression encoders. This is true for file encoders, video encoders, and audio encoders. We use it in the solar lab to optimize sample rates in data acquisition.
  
  In fact, pattern recognition and compression are very tightly coupled if you consider autoencoders and other feature extraction schemes and compare them mathematically with what compression does. See [*Data Compression – A Generic Principle of Pattern Recognition?*, Gunther Heidemann, Helge Ritter, VISIGRAPP 2008](https://link.springer.com/chapter/10.1007/978-3-642-10226-4_16)
  
  Zlib and lz4 have more like hyper-parametric learning, however they don't persist what they learn. This work is interesting: [*Adaptive On-the-Fly Compression*, Chandra Krintz, Sezgin Sucu, IEEE Parallel and Distributed Systems, v17 n1, January 2006](https://www.cs.ucsb.edu/~ckrintz/papers/acejournal.pdf).
  
  \*\*Suggested Project:
  
  Create a theoretical framework and software POC that learns correlations between these two sets.
  
  1. Quickly ascertainable features of documents or audio or video streams (i.e file path components, media titles, date, file type, and first N bytes)
  2. The existing parameters that existing open source compression software learns during its existing pattern recognition algorithms
  
  Persisting those correlations between compression invocations may considerably improve file transfer, kernel operations (since lz4 is now native in kernels like LINUX), and media streaming.
  
  How much effort is made to persist features extracted (pattern recognition) between frames in media streaming is worth investigating too.
  
  
  ---
  
  ### Answer by username_2 on 2018-05-31. Score: 0
  
  Recurrent neural networks can be trained on character level data to generate sentences which are very similar to human language. Go through this [link](http://karpathy.github.io/2015/05/21/rnn-effectiveness/). You could experiment with them for compressing text.
  
  
  ---
  
  