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Energy-based Dropout in Restricted Boltzmann Machines: Why not go random

Energy-based Dropout in Restricted Boltzmann Machines: Why not go random

IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), 2021
17 January 2021
Mateus Roder
Gustavo de Rosa
V. H. C. de Albuquerque
André Luis Debiaso Rossi
João Paulo Papa
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Energy-based Dropout in Restricted Boltzmann Machines: Why not go random"

2 / 2 papers shown
Title
Evolving Restricted Boltzmann Machine-Kohonen Network for Online
  Clustering
Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
Ieee J. Senthilnath Senior Member
A. Bhattiprolu
Ankur Singh
Bangjian Zhou
Ieee Xiaoli Min Wu Senior Member
Ieee Xiaoli J´on Atli Benediktsson Fellow
Xiaoli Li
86
0
0
14 Feb 2024
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional
  Neural Networks
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural NetworksIberian Conference on Pattern Recognition and Image Analysis (IbPRIA), 2022
C. F. G. Santos
Mateus Roder
L. A. Passos
João Paulo Papa
128
1
0
05 Mar 2022
1