Search Results for author: Ronan Perry

Found 6 papers, 3 papers with code

mvlearn: Multiview Machine Learning in Python

no code implementations25 May 2020 Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein

As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years.

BIG-bench Machine Learning

High-dimensional and universally consistent k-sample tests

no code implementations20 Oct 2019 Sambit Panda, Cencheng Shen, Ronan Perry, Jelle Zorn, Antoine Lutz, Carey E. Priebe, Joshua T. Vogelstein

The evaluation included several popular independence statistics and covered a comprehensive set of simulations.

Test Two-sample testing

MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS

no code implementations25 Sep 2019 Ronan Perry, Tyler M. Tomita, Jesse Patsolic, Benjamin Falk, Joshua Vogelstein

In particular, DFs dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to permuting feature indices.

Image Classification Time Series Analysis

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

1 code implementation25 Sep 2019 Adam Li, Ronan Perry, Chester Huynh, Tyler M. Tomita, Ronak Mehta, Jesus Arroyo, Jesse Patsolic, Benjamin Falk, Joshua T. Vogelstein

In particular, 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.

EEG Electroencephalogram (EEG) +2

Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities

1 code implementation30 Jun 2019 Ronan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell, Hayden Helm, Cencheng Shen, Joshua T. Vogelstein

Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty.

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