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1 |
Deep learning-based prediction of particle size distributions in construction and demolition waste recycling using convolutional neural networks on 3D laser triangulation data
Kroell, Nils. - Aachen : Universitätsbibliothek der RWTH Aachen, 2025
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2 |
Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning
Heinrich, Kai. - Würzburg : Universität Würzburg, 2024
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3 |
A wholistic view of continual learning with deep neural networks: forgotten lessons and the bridge to active and open world learning
Mundt, Martin. - Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2023
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4 |
Geometric deep learning and equivariant neural networks
Enthalten in Artificial intelligence review 4.6.2023: 1-58
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5 |
Deep neural networks and machine learning radiomics modelling for prediction of relapse in mantle cell lymphoma
Lisson, Catharina Silvia. - Ulm : Universität Ulm, 2022
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6 |
Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research
Bodesheim, Paul. - Jena : Friedrich-Schiller-Universität Jena, 2022
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7 |
Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study
Schilling, Maximilian. - Aachen : Universitätsbibliothek der RWTH Aachen, 2022
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8 |
Detecting and responding to hostile disinformation activities on social media using machine learning and deep neural networks
Enthalten in Neural computing & applications 9.6.2022: 1-23
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9 |
Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research—Individual identification and attribute prediction with image features from deep neural networks and decoupled decision models applied to elephants and great apes
Enthalten in Mammalian biology 12.4.2022: 1-23
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10 |
Constitutive artificial neural networks
Linka, Kevin. - Hamburg : Universitätsbibliothek der Technischen Universität Hamburg-Harburg, 2021
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