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Sparse deep neural networks for modeling aluminum electrolysis dynamics

Sparse deep neural networks for modeling aluminum electrolysis dynamics

13 September 2022
E. Lundby
Adil Rasheed
I. Halvorsen
J. Gravdahl
ArXivPDFHTML

Papers citing "Sparse deep neural networks for modeling aluminum electrolysis dynamics"

5 / 5 papers shown
Title
Sparse neural networks with skip-connections for identification of
  aluminum electrolysis cell
Sparse neural networks with skip-connections for identification of aluminum electrolysis cell
E. Lundby
Haakon Robinson
Adil Rasheed
I. Halvorsen
J. Gravdahl
22
2
0
02 Jan 2023
A novel corrective-source term approach to modeling unknown physics in
  aluminum extraction process
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
Haakon Robinson
E. Lundby
Adil Rasheed
J. Gravdahl
15
5
0
22 Sep 2022
Sparsity in Deep Learning: Pruning and growth for efficient inference
  and training in neural networks
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler
Dan Alistarh
Tal Ben-Nun
Nikoli Dryden
Alexandra Peste
MQ
139
684
0
31 Jan 2021
A Survey on Neural Network Interpretability
A Survey on Neural Network Interpretability
Yu Zhang
Peter Tiño
A. Leonardis
K. Tang
FaML
XAI
137
656
0
28 Dec 2020
Physics guided machine learning using simplified theories
Physics guided machine learning using simplified theories
Suraj Pawar
Omer San
Burak Aksoylu
Adil Rasheed
T. Kvamsdal
PINN
AI4CE
92
87
0
18 Dec 2020
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