Florian Mai
TitleCited byYear
Using titles vs. full-text as source for automated semantic document annotation
L Galke, F Mai, A Schelten, D Brunsch, A Scherp
Proceedings of the Knowledge Capture Conference, 20, 2017
82017
Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text
F Mai, L Galke, A Scherp
Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries …, 2018
52018
Multi-modal adversarial autoencoders for recommendations of citations and subject labels
L Galke, F Mai, I Vagliano, A Scherp
Proceedings of the 26th Conference on User Modeling, Adaptation and …, 2018
42018
Comparing titles vs. full-text for multilabel classification of scientific papers and news articles'
L Galke, F Mai, A Schelten, D Brunsch, A Scherp
arXiv preprint arXiv:1705.05311, 2017
12017
On the Tunability of Optimizers in Deep Learning
PT Sivaprasad, F Mai, T Vogels, M Jaggi, F Fleuret
arXiv preprint arXiv:1910.11758, 2019
2019
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
F Mai, L Galke, A Scherp
arXiv preprint arXiv:1902.06423, 2019
2019
What If We Encoded Words as Matrices and Used Matrix Multiplication as Composition Function?
L Galke, F Mai, A Scherp
INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für …, 2019
2019
Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation
I Vagliano, L Galke, F Mai, A Scherp
Proceedings of the ACM Recommender Systems Challenge 2018, 5, 2018
2018
Reranking-based Recommender System with Deep Learning
A Saleh, F Mai, C Nishioka, A Scherp
INFORMATIK 2017, 2017
2017
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Articles 1–9