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Multi-Modal Adversarial Autoencoders for Recommendations of Citations
  and Subject Labels

Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels

User Modeling, Adaptation, and Personalization (UMAP), 2018
22 July 2019
Lukas Galke
Florian Mai
Iacopo Vagliano
A. Scherp
ArXiv (abs)PDFHTML

Papers citing "Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels"

2 / 2 papers shown
A Comparison of Deep-Learning Methods for Analysing and Predicting
  Business Processes
A Comparison of Deep-Learning Methods for Analysing and Predicting Business ProcessesIEEE International Joint Conference on Neural Network (IJCNN), 2021
Ishwar Venugopal
Jessica Töllich
Michael Fairbank
A. Scherp
138
35
0
11 Feb 2021
Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and
  Inference
Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference
Lukas Galke
Iacopo Vagliano
A. Scherp
CLLGNNOnRLAI4CE
121
10
0
15 May 2019
1