Idil @idilsulo
Researcher in computer vision | 🎓 MSc @TU_Muenchen CS, BSc @METU_ODTU CENG idils.com Berlin Joined January 2018-
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Happy to be selected as an Outstanding Reviewer for #CVPR2025 🙌
Behind every great conference is a team of dedicated reviewers. Congratulations to this year’s #CVPR2025 Outstanding Reviewers! cvpr.thecvf.com/Conferences/20…
The page with the changes in this edition is not found: iccv.thecvf.com/Conferences/20… -- Could this be fixed? @ICCVConference
@yoshitomo_cs @CVPR Might be that the previous version did not get updated correctly, seems correct now after re-submission. Thanks a lot for the prompt response!
@CVPR When I submit my review, the paper summary shows up before the paper title. Is this intended?
Home Robotics'te Beko ile işbirliğinin eşiğindeyiz. Lisans ve lisansüstü araştırmacılar arıyoruz. İlgilenenlerin bir an önce başvurmalarını (bazı ufak görevlerin yapılmasını bekleyeceğiz) öneriyorum. Not: herkese ulaştırmak için takipçilerimin desteğini rica ediyorum.
If you're in Milan at #ECCV2024, check out our work #SceneTeller on high-quality 3D scene generation from natural language prompts! 🏡 Project page: sceneteller.github.io 📄 Paper: arxiv.org/abs/2407.20727 👩💻 Code: github.com/sceneteller/Sc… @eccvconf
New blog post on the original 3D Gaussian Splatting paper ✨ summergeometry.org/sgi2024/introd…
Introducing The AI Scientist: The world’s first AI system for automating scientific research and open-ended discovery! sakana.ai/ai-scientist/ From ideation, writing code, running experiments and summarizing results, to writing entire papers and conducting peer-review, The AI Scientist opens a new era of AI-driven scientific research and accelerated discovery. Here are 4 example Machine Learning research papers generated by The AI Scientist. We published our report, The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery, and open-sourced our project! Paper: arxiv.org/abs/2408.06292 GitHub: github.com/SakanaAI/AI-Sc… Our system leverages LLMs to propose and implement new research directions. Here, we first apply The AI Scientist to conduct Machine Learning research. Crucially, our system is capable of executing the entire ML research lifecycle: from inventing research ideas and experiments, writing code, to executing experiments on GPUs and gathering results. It can also write an entire scientific paper, explaining, visualizing and contextualizing the results. Furthermore, while an LLM author writes entire research papers, another LLM reviewer critiques resulting manuscripts to provide feedback to improve the work, and also to select the most promising ideas to further develop in the next iteration cycle, leading to continual, open-ended discoveries, thus emulating the human scientific community. As a proof of concept, our system produced papers with novel contributions in ML research domains such language modeling, Diffusion and Grokking. We (@_chris_lu_, @RobertTLange, @hardmaru) proudly collaborated with the @UniOfOxford (@j_foerst, @FLAIR_Ox) and @UBC (@cong_ml, @jeffclune) on this exciting project.
Felt like running SAM2 on this — does not seem quite capable of tracking fast sheep
How to match the “wiggliness” of two shapes -- The blog post based on the project we worked with the fellows at MIT Summer Geometry Initiative is up! summergeometry.org/sgi2024/how-to…
Which is like two days of progress nowadays
Also has other versions — This one is for movies: movie-map.com
Today I found the book author version of @EveryNoise project — Nice graph visualizations! literature-map.com
In this post, I share the top reads from the books I read in 2023: codingwoman.com/books-2023/
@DrTugrulcan Viral tweet research test? :) x.com/invariance/sta…
Sad to hear that one of my favorite projects on the Internet is shutting down
Unfortunately, the future of everynoise.com, in its current form, is probably short.
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