N. Loka

@nasrullohloka.bsky.social

satrialoka.github.io

1/ Wouldn't it be nice if you could perform Bayesian inference *efficiently* but also *reliably*? Amortized inference offers the former, while MCMC is often presented as the "gold standard" for accuracy and reliability. Enter the Amortized Bayesian Workflow...

Bild

1/ Another @iclr-conf.bsky.social paper thread! Do you want to make SOTA probabilistic predictions using transformers & your dataset is a *set* (not a sequence or time series), so you care about permutation invariance... but also efficiency? Keep reading, we have exactly what you need. 👇

Bild

1/ Introducing ACE (Amortized Conditioning Engine)! Our new AISTATS 2025 paper presents a transformer framework that unifies tasks from image completion to BayesOpt & simulator-based inference under *one* probabilistic conditioning approach. It's Bayes all the way down!

Bild

I had the honor to meet with Professor Shun-Ichi Amari, a pioneer in neural networks, at the University of Tokyo. Back then he worked on early neural nets using pen and paper, before running computing experiments in the future. He talked not only about AI, but also human evolution and consciousness.

Bild