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Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling.

G. Spell, S. Ren, L. Collins, and J. Malof. AAAI, page 9874-9881. AAAI Press, (2023)

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Patrick Spell

Erwin Spell

Christian Spell

Julian Spellmann

Gottfried Spellenberg

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FLOP: Federated Learning on Medical Datasets using Partial Networks.Q. Yang, J. Zhang, W. Hao, G. Spell, and L. Carin. KDD, page 3845-3853. ACM, (2021)Application of Compositional Neural Networks for Robust Classification of Infrared Imagery.G. Spell, L. Collins, and J. Malof. IGARSS, page 2799-2802. IEEE, (2021)Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images.J. Sigman, G. Spell, K. Liang, and L. Carin. CoRR, (2020)Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling.G. Spell, S. Ren, L. Collins, and J. Malof. CoRR, (2022)Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling.G. Spell, S. Ren, L. Collins, and J. Malof. AAAI, page 9874-9881. AAAI Press, (2023)Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection.K. Liang, J. Sigman, G. Spell, D. Strellis, W. Chang, F. Liu, T. Mehta, and L. Carin. CoRR, (2019)

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Disambiguation of "Spell, Gregory P."

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URL
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Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling.

Please choose a person to relate this publication to

Patrick Spell

Erwin Spell

Christian Spell

Julian Spellmann

Gottfried Spellenberg

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Spell, Gregory P."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling.

Please choose a person to relate this publication to

Patrick Spell

Erwin Spell

Christian Spell

Julian Spellmann

Gottfried Spellenberg

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling.