• Corpus ID: 236956955

Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

@inproceedings{Li2019ManifoldOR,
  title={Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks},
  author={Adam Li and Ronan Perry and Chester Huynh and Tyler M. Tomita and Ronak R. Mehta and Jes{\'u}s Arroyo and Jesse Patsolic and Benjamin Falk and Joshua T. Vogelstein},
  year={2019}
}
. Decision forests, in particular random forests and gradient boosting trees have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices. However, in structured data lying on a manifold—such as images, and time-series—deep networks, specifically convolutional deep networks… 

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