Compressive sensing as a new paradigm for model building

@inproceedings{Nelson2012CompressiveSA,
  title={Compressive sensing as a new paradigm for model building},
  author={Lance J. Nelson and Fei Zhou and Gus L. W. Hart and Vidvuds Ozoliņ{\vs}},
  year={2012}
}
The widely-accepted intuition that the important properties of solids are determined by a few key variables underpins many methods in physics. Though this reductionist paradigm is applicable in many physical problems, its utility can be limited because the intuition for identifying the key variables often does not exist or is difficult to develop. Machine learning algorithms (genetic programming, neural networks, Bayesian methods, etc.) attempt to eliminate the a priori need for such intuition… 
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