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ORCIDMatthias Griebel
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2020 – today
- 2022
[b1]Matthias Griebel:
Applied Deep Learning: from Data to Deployment. Julius Maximilians University Würzburg, Germany, 2022- 2021
- [i1]Matthias Griebel, Dennis Segebarth, Nikolai Stein, Nina Schukraft, Philip Tovote, Robert Blum, Christoph M. Flath:
Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. CoRR abs/2111.06693 (2021) - 2020
- [c8]Alexander Dürr, Matthias Griebel, Giacomo Welsch, Frédéric Thiesse:
Predicting Fraudulent initial Coin Offerings using Information Extracted from Whitepapers. ECIS 2020 - [c7]Matthias Griebel, Christoph Flath, Sascha Friesike:
Augmented Creativity: Leveraging Artificial Intelligence for Idea Generation in the Creative Sphere. ECIS 2020 - [c6]Felix Oberdorf, Nikolai Stein, Nicolas Walk, Matthias Griebel, Christoph Flath:
ADR for Big-Data IT Artifact Development: An Escalation Management Example. ICIS 2020
2010 – 2019
- 2019
- [c5]Matthias Griebel, Giacomo Welsch, Toni Greif, Christoph Flath:
A Picture is Worth More than a Thousand Purchases: Designing an Image-based Fashion Curation System. ECIS 2019 - [c4]Adrian Krenzer, Nikolai Stein, Matthias Griebel, Christoph Flath:
Augmented Intelligence for Quality Control of Manual Assembly Processes using Industrial Wearable Systems. ICIS 2019 - [c3]Matthias Griebel, Alexander Dürr, Nikolai Stein:
Applied image recognition: guidelines for using deep learning models in practice. Wirtschaftsinformatik 2019: 393-406 - 2017
- [c2]Matthias Hauser
, Matthias Griebel, Frédéric Thiesse:
A hidden Markov model for distinguishing between RFID-tagged objects in adjacent areas. RFID 2017: 167-173 - [c1]Matthias Hauser, Matthias Griebel, Jannis Hanke, Frédéric Thiesse:
Empowering Smarter Fitting Rooms with RFID Data Analytics. Wirtschaftsinformatik 2017
Coauthor Index
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