Thanks a lot for the shoutout and your kind words, Sharaj! I am happy to hear that you found my Quarto website template helpful.

Excited to share our short primer! 🚀 Stay tuned for many more applications on the horizon.

New work with a 🌟 team of collaborators! Can we make probabilistic inference FAST using modern deep-learning *amortized* methods, while retaining accuracy guarantees of MCMC? Read on to find out... (Out-take titles: - The best of both worlds - Fast and rigorous & others...)

Thank you so much for the kind shoutout, Blair! This short paper describes our vision for the next wave of amortized Bayesian workflows. Currently the software stack is PyMC + BayesFlow + PSIS + tensorflow-probability (/JAX), but we want to mold it into a single software ASAP.

An undervalued benefit of doing a PhD in deep learning: Most ML conferences/journals have fair licenses and the copyright remains with the authors. That means I don't have to chase publishers to get permission to reprint my own papers in my own PhD thesis. Academic Chatter™

Thank you so much for the shoutout! The brand-new BayesFlow version based on Keras 3 is available on the dev branch. We'll merge to main when we're feature-complete. Questions, feedback, and contributions are always welcome! Here is the Discourse forum: discuss.bayesflow.org

NeurIPS decisions are out on OpenReview! NeurIPS Conference #NeurIPS #NeurIPS2024

Our paper on consistency models for simulation-based inference has been accepted at #NeurIPS2024! Thanks to a fantastic team Valentin Pratz (co-lead), Ullrich Köthe, Paul Bürkner, Stefan Radev 📄 Preprint: arxiv.org/abs/2312.05440 🔁 Updates, code, and thread follow soon™