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Dear all,</div>
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Please see the information of our next VUML seminar (<a href="https://sites.google.com/view/vumls/home">https://sites.google.com/view/vumls/home</a><span style="font-size: 12pt;">) below:</span></div>
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Speaker: <b>Sascha Saralajew </b>(<a href="https://scholar.google.com/citations?user=YTi93_0AAAAJ&hl=de">https://scholar.google.com/citations?user=YTi93_0AAAAJ&hl=de</a><span style="font-size: 12pt;">)</span></div>
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Title: Prototype-based Classification Learning: From traditional versions to modern approaches (please see the abstract below the email)</div>
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<div class="elementToProof" style="font-size: 12pt;">Time: Monday, <span style="font-size: 12pt;">13, Oct. 10:00</span></div>
<div class="elementToProof" style="font-size: 12pt;">Room: NU-3A57</div>
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As Sascha is visiting from abroad, Filip suggests seeing if anyone is interested in having a chat with him, and you can sign up with the link below:</div>
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<a href="https://docs.google.com/spreadsheets/d/1JqcHqkktacLO8lJLNJWO1eAQrReAFOI2F7hUjXIVMcw/edit?gid=0#gid=0">https://docs.google.com/spreadsheets/d/1JqcHqkktacLO8lJLNJWO1eAQrReAFOI2F7hUjXIVMcw/edit?gid=0#gid=0</a></div>
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Looking forward to seeing all of you on Monday! </div>
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Best from the organizers,</div>
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Zhao & Daniel</div>
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——————</div>
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Abstract: Prototype-based classification learning is a powerful machine learning method with the great properties of being interpretable and at the same time provably robust. These properties make the method an interesting approach for various domains (e.g.,
biomedicine) and especially for domains classified as high-risk in the EU AI Act. In this talk, I will introduce the concept of prototype-based classification learning, starting from the traditional methods (e.g., vector quantization) to modern approaches
that combine prototype-based classification concepts with deep neural networks (e.g., ProtoPNet). In this introduction, I will focus on the properties of prototype-based classification methods, such as interpretability, and whether they are preserved in modern
approaches and other variants. I will give examples, show possibilities, and talk about open questions.</div>
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