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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Chatzimparmpas, A. ; Martins, R. M. ; Jusufi, I. ; Kucher, K. ; Rossi, F. ; Kerren, A.

Computer graphics forum, 2020-06, Vol.39 (3), p.713-756 [Peer Reviewed Journal]

Oxford: Blackwell Publishing Ltd

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2
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
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A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Cheng, Lefeng ; Yu, Tao

International journal of energy research, 2019-05, Vol.43 (6), p.1928-1973 [Peer Reviewed Journal]

Bognor Regis: Hindawi Limited

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3
Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning
Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning
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Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning

Spikol, Daniel ; Ruffaldi, Emanuele ; Dabisias, Giacomo ; Cukurova, Mutlu

Journal of computer assisted learning, 2018-08, Vol.34 (4), p.366-377 [Peer Reviewed Journal]

Oxford: Wiley-Blackwell

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4
Self-Directed Learning: A Cognitive and Computational Perspective
Self-Directed Learning: A Cognitive and Computational Perspective
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Self-Directed Learning: A Cognitive and Computational Perspective

Gureckis, Todd M. ; Markant, Douglas B.

Perspectives on psychological science, 2012-09, Vol.7 (5), p.464-481 [Peer Reviewed Journal]

Los Angeles, CA: Sage Publications

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5
Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems
Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems
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Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

Tartakovsky, A. M. ; Marrero, C. Ortiz ; Perdikaris, Paris ; Tartakovsky, G. D. ; Barajas‐Solano, D.

Water resources research, 2020-05, Vol.56 (5), p.n/a [Peer Reviewed Journal]

Washington: Blackwell Publishing Ltd

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6
Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology
Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology
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Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology

Yan, Jun ; Wang, Xiangfeng

The Plant journal : for cell and molecular biology, 2022-09, Vol.111 (6), p.1527-1538 [Peer Reviewed Journal]

Oxford: Blackwell Publishing Ltd

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7
Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography
Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography
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Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

Samala, Ravi K. ; Chan, Heang-Ping ; Hadjiiski, Lubomir ; Helvie, Mark A. ; Wei, Jun ; Cha, Kenny

Medical physics (Lancaster), 2016-12, Vol.43 (12), p.6654-6666 [Peer Reviewed Journal]

United States: American Association of Physicists in Medicine

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8
How to apply zero‐shot learning to text data in substance use research: An overview and tutorial with media data
How to apply zero‐shot learning to text data in substance use research: An overview and tutorial with media data
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How to apply zero‐shot learning to text data in substance use research: An overview and tutorial with media data

Riordan, Benjamin ; Bonela, Abraham Albert ; He, Zhen ; Nibali, Aiden ; Anderson‐Luxford, Dan ; Kuntsche, Emmanuel

Addiction (Abingdon, England), 2024-05, Vol.119 (5), p.951-959 [Peer Reviewed Journal]

England: Blackwell Publishing Ltd

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9
Deep learning with convolutional neural networks for EEG decoding and visualization
Deep learning with convolutional neural networks for EEG decoding and visualization
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Deep learning with convolutional neural networks for EEG decoding and visualization

Schirrmeister, Robin Tibor ; Springenberg, Jost Tobias ; Fiederer, Lukas Dominique Josef ; Glasstetter, Martin ; Eggensperger, Katharina ; Tangermann, Michael ; Hutter, Frank ; Burgard, Wolfram ; Ball, Tonio

Human brain mapping, 2017-11, Vol.38 (11), p.5391-5420 [Peer Reviewed Journal]

United States: John Wiley & Sons, Inc

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10
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
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Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

Knoll, Florian ; Murrell, Tullie ; Sriram, Anuroop ; Yakubova, Nafissa ; Zbontar, Jure ; Rabbat, Michael ; Defazio, Aaron ; Muckley, Matthew J. ; Sodickson, Daniel K. ; Zitnick, C. Lawrence ; Recht, Michael P.

Magnetic resonance in medicine, 2020-12, Vol.84 (6), p.3054-3070 [Peer Reviewed Journal]

United States: Wiley Subscription Services, Inc

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