Corpus ID: 3039898

Why are deep nets reversible: A simple theory, with implications for training

@article{Arora2015WhyAD,
  title={Why are deep nets reversible: A simple theory, with implications for training},
  author={Sanjeev Arora and Yingyu Liang and Tengyu Ma},
  journal={ArXiv},
  year={2015},
  volume={abs/1511.05653}
}

Sanjeev Arora, Yingyu Liang, Tengyu Ma

Published 2015

Computer Science

ArXiv

Generative models for deep learning are promising both to improve understanding of the model, and yield training methods requiring fewer labeled samples. Recent works use generative model approaches to produce the deep net's input given the value of a hidden layer several levels above. However, there is no accompanying "proof of correctness" for the generative model, showing that the feedforward deep net is the correct inference method for recovering the hidden layer given the input… CONTINUE READING

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