Presentation Open Access
Early Career Research Consortium
Sylvia Melzer
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<subfield code="a">The Early Career Research Consortium was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2176 'Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures', project no. 390893796.</subfield>
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<subfield code="a"><p>The early career research consortium has provided young researchers from any subject area within AI with the opportunity to present their ideas and receive feedback at an early stage of at their scientific work. We have invited young researchers to present their research and established connections with new researchers.</p>
<p>The early career research consortium has also provided to discuss their research interests and career objectives with established researchers in AI and to network with other participants.</p>
<p>The accepted contributions are:</p>
<ul>
<li>
<p>Jan Martin Spreitzenbarth: <strong>KI 2021 DC: AI Methods in Procurement </strong>(<a href="https://www.fdr.uni-hamburg.de/record/9613/files/KI2021_ECR_submission1.zip?download=1">download submission</a>)<strong> </strong></p>
</li>
<li>
<p>Juliane Ressel:<strong> Adoption of Artificial Intelligence in the Insurance Sector: Creating a Governance Framework to Ensure Consumer Protection</strong> (<a href="https://www.fdr.uni-hamburg.de/record/9613/files/KI2021_ECR_submission2.zip?download=1">download submission</a>)</p>
</li>
<li>
<p>Sumaiya Suravee: <strong> Methods for Automated Learning of Semantic Structures from Textual Data</strong><br>
(<a href="https://www.fdr.uni-hamburg.de/record/9613/files/KI2021_ECR_submission3.zip?download=1">download submission</a>)</p>
</li>
<li>
<p>Honghu Xue: <strong>Efficient Deep Reinforcement Learning Using Model-based RL </strong>(<a href="https://www.fdr.uni-hamburg.de/record/9613/files/KI2021_ECR_submission4.zip?download=1">download submission</a>)</p>
</li>
<li>
<p>Vadym Gryshchuk: <strong>Encoding of Semantic Knowledge in Convolutional Neural Networks: Visual and Textual Explanations </strong></p>
</li>
<li>
<p>Mena Leemhuis: <strong>Cone-based Embeddings for Logics with Negation</strong> (<a href="https://www.fdr.uni-hamburg.de/record/9613/files/KI2021_ECR_submission6.zip?download=1">download submission</a>)</p>
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<p>The submitted abstracts and presentations are in this upload for which permission to publish has been granted.</p></subfield>
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- Publication date:
- September 27, 2021
- DOI:
-
- Keyword(s):
- UWA CSMC KI2021 AI DL01 Data Linking ECR
- Meeting:
- 44th German Conference on Artificial Intelligence (KI2021), Virtual Conference, September 27 – Oct 1, 2021 (Session Early Career Research Consortium )
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International