Mehmet Saygın Seyfioğlu @mehsaygin
Ph.D. candidate at @UW, working on medical AI at the intersection of vision and language. Previously 4x Research Intern @AmazonScience. mehmetsayginseyfioglu.github.io Seattle, WA Joined May 2013-
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Excited to share that Quilt-LLaVA has been accepted at @CVPR! As promised, we are making our model, datasets, and training scripts available to the community: quilt-llava.github.io See you all in Seattle! @wizdom_dominic @fghezloo @RanjayKrishna @lindashapiro
Excited to share that Quilt-LLaVA has been accepted at @CVPR! As promised, we are making our model, datasets, and training scripts available to the community: quilt-llava.github.io See you all in Seattle! @wizdom_dominic @fghezloo @RanjayKrishna @lindashapiro
Thank you @_akhaliq for sharing our work! Check out Diffuse2Choose, a virtual try-all model that allows users to try on any e-commerce item in any setting! Arxiv: arxiv.org/abs/2401.13795 Website: diffuse2choose.github.io Thread: x.com/mehsaygin/stat… Stay tuned for more!
Thank you @_akhaliq for sharing our work! Check out Diffuse2Choose, a virtual try-all model that allows users to try on any e-commerce item in any setting! Arxiv: arxiv.org/abs/2401.13795 Website: diffuse2choose.github.io Thread: x.com/mehsaygin/stat… Stay tuned for more!
Thank you @Scobleizer, for covering our work! Check out Diffuse2Choose, a virtual try-all model that allows users to try on any e-commerce item in any setting! 📜: arxiv.org/abs/2401.13795 🌎: diffuse2choose.github.io Thread: x.com/mehsaygin/stat… Stay tuned for more!
Thank you @Scobleizer, for covering our work! Check out Diffuse2Choose, a virtual try-all model that allows users to try on any e-commerce item in any setting! 📜: arxiv.org/abs/2401.13795 🌎: diffuse2choose.github.io Thread: x.com/mehsaygin/stat… Stay tuned for more!
Thank you @thibaudz, for covering our work! Please see the following thread for more information! x.com/mehsaygin/stat…
Thank you @thibaudz, for covering our work! Please see the following thread for more information! x.com/mehsaygin/stat…
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Inexpensive token generation and agentic workflows for large language models (LLMs) open up intriguing new possibilities for training LLMs on synthetic data. Pretraining an LLM on its own directly generated responses to prompts doesn't help. But if an agentic workflow implemented…
A thread on the artwork by Japanese artist Hisao Kawada
Why do diffusion models use the U-Net architecture? Fascinatingly, the U-Net's encoder-decoder structure with long skip connections naturally encodes the belief propagation algorithm for hierarchical models. Explore more in this paper! arxiv.org/abs/2404.18444. .
Delighted to share ✨Med-Gemini✨ - our new family of multimodal models for medicine unlocking new possibilities for health - arxiv.org/pdf/2404.18416 More accurate multimodal conversations about medical images🩻, surgical videos📽️, genomics🧬, ultra-long health records📚, ECGs🫀…
Daniel Dennett’s death has made me reflect on the work I’m doing. For a few years, with the increased pace of things, I’ve felt it’s impossible for me to take the time to think clearly enough to make a point that will last more than two years. This seems like a bad thing.
A side note: We started this project about three years ago. I am really proud of how it has evolved over the years, and excited to share this with everyone!
1. 🧵🎉 Excited to share that our paper "Sequential Compositional Generalization in Multimodal Models" is accepted as a long paper at #NAACL2024! 🌟 We'll be presenting our findings in Mexico City this June (@naaclmeeting). Dive into the full details here 👇 - Paper:…
1. 🧵🎉 Excited to share that our paper "Sequential Compositional Generalization in Multimodal Models" is accepted as a long paper at #NAACL2024! 🌟 We'll be presenting our findings in Mexico City this June (@naaclmeeting). Dive into the full details here 👇 - Paper:…
Good work in mitigating the memorization risks of Diffusion models. It worth to examine the mitigation under stronger attacks. Our recent work, Shake to Leak (S2L, arxiv.org/abs/2403.09450), provides a simple yet effective way to red-team the privacy of Diffusion models: Can your…
Consistent Diffusion Meets Tweedie. Our latest paper introduces an exact framework to train/finetune diffusion models like Stable Diffusion XL solely with noisy data. A year's worth of work breakthrough in reducing memorization and its implications on copyright 🧵
This is the kind of things that gets both roboticist and everyone else excited about robotics and AI!
Boston Dynamics reveals its new, improved all-electric Atlas robot, and @bheater has all the details right here: tcrn.ch/3vKUeeN
Our computer vision textbook is released! Foundations of Computer Vision with Antonio Torralba and Bill Freeman mitpress.mit.edu/9780262048972/… It’s been in the works for >10 years. Covers everything from linear filters and camera optics to diffusion models and radiance fields. 1/4
I've spent the past ~2 weeks trying to make a chip from scratch with no prior experience. It's been an incredible source of learning so far. Progress tracker in thread (coolest stuff at the end)👇
Our latest study measures how persuasive language models like Claude are compared to humans. We find a general scaling trend: newer models tend to be more persuasive, with Claude 3 Opus generating arguments that don't differ statistically from human-written ones.
New Anthropic research: Measuring Model Persuasiveness We developed a way to test how persuasive language models (LMs) are, and analyzed how persuasiveness scales across different versions of Claude. Read our blog post here: anthropic.com/news/measuring…
🔥New Video🔥 Flow matching (not classic diffusion) is the basis for state-of-the-art text to image models, like Stable Diffusion 3. Here is how it works: youtu.be/7NNxK3CqaDk
Three is a magic number: Professor Su-In Lee earns trio of honors, including the “Korean Nobel Prize” in engineering, for advancing AI for biomedicine news.cs.washington.edu/2024/04/03/thr…
Update on 🔮Readout Guidance (readout-guidance.github.io)! We open sourced the code – check out our demos, model weights, and training code: github.com/google-researc…. Here’s a teaser of what you can do with our method:
Proof that you don't need Olympiad golds for building towards a better Devin if you have open source. (although at Princeton, Olympiad medalists are so commonplace that even if they exist, they don't bother to mention them) github.com/princeton-nlp/…
Updated list of diffusion model tutorial sources, from my lecture on diffusion.
I wrote a tutorial on diffusion models for undergrad and grad students. I tried my best to give intuitive explanations for complicated equations. Your feedback is much appreciated Thanks to those who suggested various reading materials to me arxiv.org/abs/2403.18103
we read "Learning by Reconstruction Produces Uninformative Features For Perception" in our reading group today. does anyone have any idea how to interpret this figure? how is this a loss curve? do we read it from right to left? paper: arxiv.org/abs/2402.11337
Check out our new work, "Health Equity LLM Toolbox", where we introduce a rigorous human evaluation framework for LLMs in healthcare, alongside the largest human evaluation to date. arxiv.org/abs/2403.12025
Jensen Huang effectively confirms GPT4 is 1.8 trillion parameters lol
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