Stefan Scholz
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stefan-scholz.bsky.social
Stefan Scholz
@stefan-scholz.bsky.social
32 followers 20 following 5 posts

Doctoral Researcher | Data and Machine Learning Enthusiast

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Mathematics 20%
Engineering 19%
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Reposted by Nils B. Weidmann, Stefan Scholz

mianahrgang.bsky.social
Mia Nahrgang @mianahrgang.bsky.social · Jun 10
📣 Happy to announce the publication of our article (w @nilsweidmann.bsky.social, @friederikeq.bsky.social, @sebnagel.bsky.social, @yannistheocharis.bsky.social & Molly Roberts) on the complexity and availability of community guidelines @icwsm.bsky.social! 🔗 ojs.aaai.org/index.php/IC...

Reposted by Nils B. Weidmann, Stefan Scholz

mianahrgang.bsky.social
Mia Nahrgang @mianahrgang.bsky.social · May 28
🚨🚀 Looking for a comparative dataset on social media platforms? We’re excited to launch COMPARE! This is a collaborative effort by @nilsweidmann.bsky.social , @friederikeq.bsky.social , @sebnagel.bsky.social , @yannistheocharis.bsky.social & Molly Roberts. 🧵⤵️ (1/5)

Reposted by Stefan Scholz

Matías Piqueras @matiaspiqueras.bsky.social · Apr 20
Sharing our new preprint "An Image is Worth K Topics: A Visual Structural Topic Model with Image Embeddings" with @mansmag.bsky.social @matmagnani.bsky.social Alexandra Segerberg and Nataša Sladoje. Available on ArXiv: arxiv.org/abs/2504.10004

Reposted by Jochen Müller, Stefan Scholz, Christian Breunig , and 1 more

nilsweidmann.bsky.social
Nils Weidmann @nilsweidmann.bsky.social · Mar 4
Want to work with us? stellen.uni-konstanz.de/jobposting/8...
Postdoctoral Position in Computational Social Science
Deadline: 15.03.2025
stellen.uni-konstanz.de

Reposted by Stefan Scholz

nilsweidmann.bsky.social
Nils Weidmann @nilsweidmann.bsky.social · Dec 9
Finally out in @polanalysis.bsky.social (w/ @stefan-scholz.bsky.social, @zacharyst.bsky.social, @keremoglu.bsky.social and Bastian Goldlücke): "Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes" Available #OpenAccess at doi.org/10.1017/pan....
Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes | Political Analysis | Cambridge Core
Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes
doi.org

Reposted by Stefan Scholz

polanalysis.bsky.social
Political Analysis @polanalysis.bsky.social · Dec 9
Now in FirstView: “Improving Computer Vision Interpretability: Transparent Two-Level Classification for Complex Scenes.” @stefan-scholz.bsky.social, @nilsweidmann.bsky.social‬, @zacharyst.bsky.social, @keremoglu.bsky.social, and Bastian Goldlücke propose a two-level method for image classification.

stefan-scholz.bsky.social
Stefan Scholz @stefan-scholz.bsky.social · Oct 25
Do you want to try out the method with your own images? Here is our free demo application. huggingface.co/spaces/ciass...
Protest Segments - a Hugging Face Space by ciass
Discover amazing ML apps made by the community
huggingface.co

stefan-scholz.bsky.social
Stefan Scholz @stefan-scholz.bsky.social · Oct 25
The novelty of this method is that it provides new insights for comparative politics: While persons, flags and signboard are important objects in protest images, particular features of protest differ across countries and protest episodes. Our method can detect these.

stefan-scholz.bsky.social
Stefan Scholz @stefan-scholz.bsky.social · Oct 25
Our method detects objects present in images, creates feature vectors from those objects and uses them as input for machine learning classifiers. We tested this on a new dataset of 140k images to predict which ones show protest. The accuracy is roughly on par with popular CNNs.

stefan-scholz.bsky.social
Stefan Scholz @stefan-scholz.bsky.social · Oct 25
While existing classifiers for images reach high levels of accuracy, it is difficult to systematically assess the visual features on which they base their classification. This problem is especially pressing for complex images that contain many different types of objects.