Deep Learning

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Beteiligte Personen
M.Sc. Ahmed Abouelazm
M.Sc. Daniel Bogdoll
M.Sc. Marcus Fechner
M.Sc. Tim Joseph
M.Sc. Nico Lambing
M.Sc. Ferdinand Mütsch
M.Sc. Stefan Orf/en
M.Sc. Stefan Orf
M.Sc. Svetlana Pavlitskaya
M.Sc. Rupert Polley
M.Sc. Nikolai Polley
M.Sc. Nicholas Popovic
M.Sc. Zhan Qu
M.Sc. Helen Schneider
M.Sc. Helen Schneider/en
M. Sc. Albert Schotschneider
M.Sc. Philipp Stegmaier
Prof. Dr. York Sure-Vetter
M.Sc. Abhishek Vivekanandan

Aktive Projekte

Veröffentlichungen zum Forschungsgebiet

Michael Färber, Coutinho, Shuzhou Yuan
Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation
Scientometrics, 2023

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Nicholas Popovic, Michael Färber
Few-Shot Document-Level Relation Extraction
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics

Igor Shapiro, Tarek Saier, Michael Färber
Sequence Labeling for Citation Field Extraction from Cyrillic Script References
Proceedings of the AAAI Workshop on Scientific Document Understanding (SDU∂AAAI'22), ACM

Michael Färber, Nicolas Weber
When to Use Which Neural Network? Finding the Right Neural Network Architecture for a Research Problem
Proceedings of the AAAI Workshop on Scientific Document Understanding (SDU∂AAAI'22), ACM

Nicholas Popovic, Walter Laurito, Michael Färber
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), Association for Computational Linguistics

Michael Färber, Vinzenz Zinecker, Isabela Bragaglia, Sebastian Celis, Maria Duma
C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations
Proceedings of the 1st International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K'21∂WWW'21), ACM

Kevin Förderer, Mischa Ahrens, Kaibin Bao, Ingo Mauser, Hartmut Schmeck
Towards the Modeling of Flexibility Using Artificial Neural Networks in Energy Management and Smart Grids
In ACM, Proceedings of the Ninth International Conference on Future Energy Systems (e-Energy '18), Seiten: 85-90, ACM, New York, NY, USA, Juni, 2018

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Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised. (From