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Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
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Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

Skrede, Ole-Johan ; De Raedt, Sepp ; Kleppe, Andreas ; Hveem, Tarjei S ; Liestøl, Knut ; Maddison, John ; Askautrud, Hanne A ; Pradhan, Manohar ; Nesheim, John Arne ; Albregtsen, Fritz ; Farstad, Inger Nina ; Domingo, Enric ; Church, David N ; Nesbakken, Arild ; Shepherd, Neil A ; Tomlinson, Ian ; Kerr, Rachel ; Novelli, Marco ; Kerr, David J ; Danielsen, Håvard E

The Lancet (British edition), 2020-02, Vol.395 (10221), p.350-360 [Periódico revisado por pares]

England: Elsevier Ltd

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Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
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Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

Tang, Hongwei ; Rabault, Jean ; Kuhnle, Alexander ; Wang, Yan ; Wang, Tongguang

Physics of fluids (1994), 2020-05, Vol.32 (5) [Periódico revisado por pares]

Melville: American Institute of Physics

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Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
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Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Cote-Allard, Ulysse ; Fall, Cheikh Latyr ; Drouin, Alexandre ; Campeau-Lecours, Alexandre ; Gosselin, Clement ; Glette, Kyrre ; Laviolette, Francois ; Gosselin, Benoit

IEEE transactions on neural systems and rehabilitation engineering, 2019-04, Vol.27 (4), p.760-771 [Periódico revisado por pares]

United States: IEEE

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4
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
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Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach

Rabault, Jean ; Kuhnle, Alexander

Physics of fluids (1994), 2019-09, Vol.31 (9) [Periódico revisado por pares]

Melville: American Institute of Physics

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Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia
Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia
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Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Picon, Artzai ; Irusta, Unai ; Álvarez-Gila, Aitor ; Aramendi, Elisabete ; Alonso-Atienza, Felipe ; Figuera, Carlos ; Ayala, Unai ; Garrote, Estibaliz ; Wik, Lars ; Kramer-Johansen, Jo ; Eftestøl, Trygve Lin, Steve

PloS one, 2019-05, Vol.14 (5), p.e0216756-e0216756 [Periódico revisado por pares]

United States: Public Library of Science

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The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem
The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem
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The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem

Colbrook, Matthew J ; Antun, Vegard ; Hansen, Anders C

Proceedings of the National Academy of Sciences - PNAS, 2022-03, Vol.119 (12), p.e2107151119-e2107151119 [Periódico revisado por pares]

United States: National Academy of Sciences

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Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems
Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems
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Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems

Belagoune, Soufiane ; Bali, Noureddine ; Bakdi, Azzeddine ; Baadji, Bousaadia ; Atif, Karim

Measurement : journal of the International Measurement Confederation, 2021-06, Vol.177, p.109330, Article 109330 [Periódico revisado por pares]

London: Elsevier Ltd

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8
A convolutional neural network approach to deblending seismic data
A convolutional neural network approach to deblending seismic data
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A convolutional neural network approach to deblending seismic data

Sun, Jing ; Slang, Sigmund ; Elboth, Thomas ; Larsen Greiner, Thomas ; McDonald, Steven ; Gelius, Leiv J

Geophysics, 2020-07, Vol.85 (4), p.WA13-WA26 [Periódico revisado por pares]

Society of Exploration Geophysicists

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9
Designing deep learning studies in cancer diagnostics
Designing deep learning studies in cancer diagnostics
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Designing deep learning studies in cancer diagnostics

Kleppe, Andreas ; Skrede, Ole-Johan ; De Raedt, Sepp ; Liestøl, Knut ; Kerr, David J ; Danielsen, Håvard E

Nature reviews. Cancer, 2021-03, Vol.21 (3), p.199-211 [Periódico revisado por pares]

England: Nature Publishing Group

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Predicting mortgage default using convolutional neural networks
Predicting mortgage default using convolutional neural networks
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Predicting mortgage default using convolutional neural networks

Kvamme, Håvard ; Sellereite, Nikolai ; Aas, Kjersti ; Sjursen, Steffen

Expert systems with applications, 2018-07, Vol.102, p.207-217 [Periódico revisado por pares]

New York: Elsevier Ltd

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