This is a beautiful analysis of the order in which diffusion models resolve uncertainty about information at different abstraction levels in different representation spaces, with significant implications for latent diffusion.
https://t.co/2nzL1Gepb8
Fun weekend project-there’s a gap between what protein structure models predict and what decades of structural biology have taught us
Found that known interface interactions can help guide protein complex assembly, but incorrect or overly rigid ones can steer us wrong
Fine-tuning on this kind of interface evidence can improve future predictions and reduce epistemic uncertainty around protein–protein interactions
Idea grew out of TAing @chang_c_liu class. More reason to get ML+bio folks together to identify these gaps
Over the past few weeks, we have been putting Gemini 4 Argon to the test on protein engineering workflows at Google DeepMind, and we have some interesting initial (zero shot!) results to share. 🧵👇
Hopefully this makes it easier for new entrants into SBI, especially those with design-dependent simulators. Open for collaborations and questions. Code coming soon
Excited to share the final paper of my PhD! Driven by challenges in modeling large systems biology models with diffusion transformers, we developed a framework to overcome difficulties in 1) compositional sampling, 2) hierarchical sampling and 3) the sim-2-real gap 🤖 ↔️ 🌍
This is a lot because it's the culmination of trying simpler things and realizing that the SBI toolbox didn't quite have everything I needed for my domain applications. eg what good is compositional sampling if your probabilistic model is misspecified?