Deep Learning

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=Deep Learning=

Beteiligte Personen
Dr. Mehwish Alam
Danilo Dessì
Prof. Dr. York Sure-Vetter

Aktive Projekte

Veröffentlichungen zum Forschungsgebiet

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