Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
Pavel Procházka
· 1 citation
AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 8 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This work composition the observables'likelihoods in per-task free-routed last-layer beliefs on a shared backbone absorbs unit-dependent loss scaling into likelihood parameters learned in the same gradient pass, and results land where theory puts them.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.