@majhas.bsky.social
PhD Student at Mila & University of Montreal | Generative modeling, sampling, molecules majhas.github.io
(1/n)🚨Train a model solving DFT for any geometry with almost no training data Introducing Self-Refining Training for Amortized DFT: a variational method that predicts ground-state solutions across geometries and generates its own training data! 📜 arxiv.org/abs/2506.01225 💻 github.com/majhas/self-...
New preprint! 🧠🤖 How do we build neural decoders that are: ⚡️ fast enough for real-time use 🎯 accurate across diverse tasks 🌍 generalizable to new sessions, subjects, and even species? We present POSSM, a hybrid SSM architecture that optimizes for all three of these axes! 🧵1/7
🧵(1/7) Have you ever wanted to combine different pre-trained diffusion models but don't have time or data to retrain a new, bigger model? 🚀 Introducing SuperDiff 🦹♀️ – a principled method for efficiently combining multiple pre-trained diffusion models solely during inference!
🔊 Super excited to announce the first ever Frontiers of Probabilistic Inference: Learning meets Sampling workshop at #ICLR2025 @iclr-conf.bsky.social! 🔗 website: sites.google.com/view/fpiwork... 🔥 Call for papers: sites.google.com/view/fpiwork... more details in thread below👇 🧵
Now you can generate equilibrium conformations for your small molecule in 3 lines of code with ET-Flow! Awesome effort put in by @fntwin.bsky.social!
Yoon@jyoonlee.bsky.social · 2y agoWe’re excited to present ET-Flow at #NeurIPS 2024—an Equivariant Flow Matching model that combines simplicity, efficiency, and precision to set a new standard for 3D molecular conformer generation. 🔖Paper: arxiv.org/abs/2410.22388 🔗Github: github.com/shenoynikhil...
ET-Flow shows, once again, that equivariance is better than Transformer when physical precision matters! come see us at @neuripsconf.bsky.social !!
Yoon@jyoonlee.bsky.social · 2y agoWe’re excited to present ET-Flow at #NeurIPS 2024—an Equivariant Flow Matching model that combines simplicity, efficiency, and precision to set a new standard for 3D molecular conformer generation. 🔖Paper: arxiv.org/abs/2410.22388 🔗Github: github.com/shenoynikhil...
Excited to share our work! I had a wonderful time collaborating with these brilliant people
Yoon@jyoonlee.bsky.social · 2y agoWe’re excited to present ET-Flow at #NeurIPS 2024—an Equivariant Flow Matching model that combines simplicity, efficiency, and precision to set a new standard for 3D molecular conformer generation. 🔖Paper: arxiv.org/abs/2410.22388 🔗Github: github.com/shenoynikhil...