8 citations · 14 across the 5 of their papers we have counts for
5 papers
Robust Similarity and Distance Learning via Decision Forests
Tyler M. Tomita, Joshua T. Vogelstein
Canonical distances such as Euclidean distance often fail to capture the appropriate relationships between items, subsequently leading to subpar inference and prediction. Many algo…
Simple Lifelong Learning Machines
Jayanta Dey, Joshua T. Vogelstein, Hayden S. Helm +13
In lifelong learning, data are used to improve performance not only on the present task, but also on past and future (unencountered) tasks. While typical transfer learning algorith…
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
Adam Li, Ronan Perry, Chester Huynh +6
Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learn…
Forest Packing: Fast, Parallel Decision Forests
James Browne, Tyler M. Tomita, Disa Mhembere +2
Machine learning has an emerging critical role in high-performance computing to modulate simulations, extract knowledge from massive data, and replace numerical models with efficie…
Sparse Projection Oblique Randomer Forests
Tyler M. Tomita, James Browne, Cencheng Shen +8
Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision…