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Exploiting random projections and sparsity with random forests and
  gradient boosting methods -- Application to multi-label and multi-output
  learning, random forest model compression and leveraging input sparsity

Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity

26 April 2017
Arnaud Joly
ArXiv (abs)PDFHTML

Papers citing "Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity"

3 / 3 papers shown
Crowd Scene Analysis by Output Encoding
Crowd Scene Analysis by Output Encoding
Yao Xue
Siming Liu
Yonghui Li
Xueming Qian
180
5
0
27 Jan 2020
Training Convolutional Neural Networks and Compressed Sensing End-to-End
  for Microscopy Cell Detection
Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection
Yao Xue
G. Bigras
J. Hugh
Nilanjan Ray
169
26
0
07 Oct 2018
Cell Detection in Microscopy Images with Deep Convolutional Neural
  Network and Compressed Sensing
Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing
Yao Xue
Nilanjan Ray
205
22
0
10 Aug 2017
1
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