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Learnergy: Energy-based Machine Learners
v1v2 (latest)

Learnergy: Energy-based Machine Learners

16 March 2020
Mateus Roder
Gustavo de Rosa
João Paulo Papa
ArXiv (abs)PDFHTML

Papers citing "Learnergy: Energy-based Machine Learners"

3 / 3 papers shown
Title
Fusing Multi- and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring with Self-Supervised and Hierarchical Deep Learning
Fusing Multi- and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring with Self-Supervised and Hierarchical Deep Learning
Nicholas LaHaye
Kelly M. Luis
Michelle M. Gierach
52
0
0
03 Oct 2025
Energy-based Dropout in Restricted Boltzmann Machines: Why not go random
Energy-based Dropout in Restricted Boltzmann Machines: Why not go randomIEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), 2021
Mateus Roder
Gustavo de Rosa
V. H. C. de Albuquerque
André Luis Debiaso Rossi
João Paulo Papa
AI4CE
151
4
0
17 Jan 2021
Fast Ensemble Learning Using Adversarially-Generated Restricted
  Boltzmann Machines
Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann Machines
Gustavo de Rosa
Mateus Roder
João Paulo Papa
OOD
58
1
0
04 Jan 2021
1