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Very cool post (with lots of intuition) about the reasoning about learning a diffusion model in latent space. Representation learning and Reconstruction are two different tasks.

Nice work presenting Kangfu Mei !

📄 “Stochastic Deep Restoration Priors for Imaging Inverse Problems” was accepted to ICML 2025 (ICML Conference)! Joint work with Yuyang Hu, Albert Peng, Weijie Gan, and colleagues from the Google’s Computational Imaging team, mauricio delbracio and Peyman Milanfar. wustl-cig.github.io/sharpwww/

Happy to announce that our work, ShaRP, has been accepted to #ICML2025! 🎉 Huge thanks to my collaborators Ulugbek S. Kamilov mauricio delbracio Peyman Milanfar Weijie Gan Albert Peng. Can't wait to see everyone in Vancouver!

TMLR's bar is not lower. The car is drawn differently to remove subjectivity from the acceptance criteria. I have read many good TMLR papers and many disastrous ICML papers. In fact I think those disastrous ICML papers wouldn't stand a chance at TMLR.

If you submitted to ICML and TMLR as an author, where did you get higher review quality (grounded, fair, and helped improve your paper)? 𝐏𝐥𝐞𝐚𝐬𝐞 𝐝𝐞𝐜𝐨𝐮𝐩𝐥𝐞 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐟𝐚𝐢𝐭𝐡 𝐨𝐟 𝐩𝐚𝐩𝐞𝐫 𝐟𝐫𝐨𝐦 𝐲𝐨𝐮𝐫 𝐣𝐮𝐝𝐠𝐞𝐦𝐞𝐧𝐭 𝐨𝐟 𝐪𝐮𝐚𝐥𝐢𝐭𝐲.

👀 notes by Gabriel Peyré

"Soy inmigrante. Mis padres son refugiados de Chile. Huimos de una dictadura y tuve el privilegio de crecer en Estados Unidos tras recibir asilo en Dinamarca. Defiendo esas protecciones, siempre". - Pedro Pascal en Cannes. #Cannes2025

South America > Europe.

KHIPU is more than a conference; it’s a vibrant community! With the spirit of collaboration at its core, we are proud to announce the launch of the second edition of the KHIPUx program, designed to expand this spirit across the heart of Latin America.

KHIPU is more than a conference; it’s a vibrant community! With the spirit of collaboration at its core, we are proud to announce the launch of the second edition of the KHIPUx program, designed to expand this spirit across the heart of Latin America.

Maybe the limit can be pushed a little bit down... like 25 --> 10?

Announcing Ambient Diffusion Omni — a framework that uses synthetic, low-quality, and out-of-distribution data to improve diffusion models. State-of-the-art ImageNet performance. A strong text-to-image results in just 2 days on 8 GPUs. Filtering ❌ Clever data use ✅

📢 We're hiring Research Scientists at Google ! Join our team to develop cutting-edge generative models for imaging & computational photography, impacting millions of users. Apply now! +info: google.com/about/careers/…

I really enjoyed collaborating with Peyman Milanfar on this paper about denoisers! 🚀 Writing it helped a lot to deepen my understanding and distill my knowledge/intuition. For anyone new to the power of image denoisers, I think this is a great intro (though I might be biased!) 👇

⚠️ Deadline Extension for KHIPUx! 🗓️New deadline: July 14 📎 +info at: khipu.ai/khipux

+1000 this 👇🏼👇🏼

For better consistency and fewer artifacts in im2im diffusion models, consider Kernel Density Steering (KDS): It is inference time and training-free; uses an N-particle ensemble to derive an empirical density, & explicit mode-seeking to push samples to high-density regions 1/2

Excited to announce my work during Google AI internship is now on arXiv! Grateful for my incredible hosts and collaborators: mauricio delbracio, Peyman Milanfar, Kangfu Mei, Mojtaba Sahraee-Ardakan and Ulugbek S. Kamilov. Please check out the paper here: [arxiv.org/abs/2507.05604].