Deep Unsupervised Learning using Nonequilibrium Thermodynamics

@inproceedings{SohlDickstein2015DeepUL,
  title={Deep Unsupervised Learning using Nonequilibrium Thermodynamics},
  author={Jascha Sohl-Dickstein and Eric A. Weiss and Niru Maheswaranathan and Surya Ganguli},
  booktitle={ICML},
  year={2015}
}
A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative… CONTINUE READING
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