Derya Soydaner (@DSoydaner) header
Derya Soydaner (@DSoydaner) profile picture

Derya Soydaner

@DSoydaner

Assistant Professor at Leiden Institute of Advanced Computer Science @LIACS • Former Postdoc @KU_Leuven • PhD in Statistics @msgsuniversite

 Leuven, Belgium
deryasoydaner.com/
Joined February 2019
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Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)Photo by Derya Soydaner (@DSoydaner)
AI, art, and Rome ✨ I’ll give a talk at the 4th FAPER Workshop (@iciapconf ) on our paper: 𝗛𝗮𝘃𝗲 𝗟𝗮𝗿𝗴𝗲 𝗩𝗶𝘀𝗶𝗼𝗻-𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗠𝗮𝘀𝘁𝗲𝗿𝗲𝗱 𝗔𝗿𝘁 𝗛𝗶𝘀𝘁𝗼𝗿𝘆? 📅 September 15, afternoon session 👉 Paper: arxiv.org/pdf/2409.03521
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Exciting news! I’m joining the Leiden Institute of Advanced Computer Science (LIACS) as an Assistant Professor! 🚀 I am also grateful for my time @KU_Leuven and the more than three years I spent in Belgium. Here’s to new beginnings – to the Netherlands!
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Deep neural networks perform well in object recognition, but do they perceive objects like humans? We show that the classification performance of CNNs deteriorates with partial removal of the contours in incomplete polygons. For more details: esann.org/sites/default/file…
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Derya Soydaner retweeted
The Nobel Prize (@NobelPrize) profile picture
The Nobel Prize
@NobelPrize8 Oct 2024
BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
Image from The Nobel Prize (@NobelPrize)
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Thanks for your interest in our work at the Vision for Art (VISART) workshop at #ECCV2024 today! Feel free to reach out if you’d like to discuss #backflip further @eccvconf
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Our poster is ready for #ECCV2024. Come and find us in Amber 1 from 10:30 to 11-15am @eccvconf
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Great first day at #ECCV2024 with inspiring sessions! Looking forward to more over the next few days @eccvconf
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Excited to present our paper at the Vision for Art (VISART) VII workshop, part of #ECCV2024 next week! See you in Milan ✈️ GitHub: github.com/gmuradas99/backfl… Paper: arxiv.org/pdf/2408.14173
Image from Derya Soydaner (@DSoydaner)
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Here we go again! September in academia - feels like we have already been through a whole year. Wishing everyone an inspiring and successful year ahead! 📚 #academiclife #NewBeginnings
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Big thanks to everyone who came to see my poster at #ECVP2024! Grateful for the incredible interest and valuable feedback on my work on Explainable AI. Looking forward to the rest of the conference and more great discussions 🙌
Image from Derya Soydaner (@DSoydaner)
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🎨Excited to announce our revised paper, 'From Paintbrush to Pixel: A Review of Deep Neural Networks in AI-Generated Art', in collaboration with my colleague Anne-Sofie Maerten! Check it out on arXiv: arxiv.org/abs/2302.10913v2
Image from Derya Soydaner (@DSoydaner)
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To future @FWOVlaanderen postdoctoral fellowship candidates, I'd like to share some insights from my experience with the FWO this year.
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more replies
However, it appears that FWO places significant importance on having a concrete mobility plan outlined in the project proposal.
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To future candidates, I suggest incorporating detailed mobility plans into your projects. This can be done relatively easily. Without this, your efforts in research, the quality of your work, and the intense labor involved in writing the project proposal might be in vain.
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Derya Soydaner retweeted
Esra Yüksel (@runmonarun) profile picture
Esra Yüksel@runmonarun25 Jun 2024
🚀 Here is our new study on the Nuclear mass predictions using machine learning models 💥In collaboration with @DSoydaner and Hüseyin Bahtiyar. Editors' suggestion 😊 journals.aps.org/prc/abstrac… – at Guildford, England
Nuclear mass predictions using machine learning models

Nuclear mass predictions using machine learning models

Understanding nuclear properties away from the stability line and near the limits of the nuclear landscape relies heavily on theoretical calculations, because many unstable nuclei are difficult to...

journals.aps.org
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