3 papers
cs.LG2026
Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
Albus Yizhuo Li, Matthew Wicker
Foundation models are increasingly being deployed in contexts where understanding the uncertainty of their outputs is critical to ensuring responsible deployment. While Bayesian me…
cs.LG2025
Bayesian Mixture-of-Experts: Towards Making LLMs Know What They Don't Know
Albus Yizhuo Li
The Mixture-of-Experts (MoE) architecture has enabled the creation of massive yet efficient Large Language Models (LLMs). However, the standard deterministic routing mechanism pres…
cs.LG2025
Kryptonite-N: Machine Learning Strikes Back
Albus Li, Nathan Bailey, Will Sumerfield +1
Quinn et al propose challenge datasets in their work called ``Kryptonite-N". These datasets aim to counter the universal function approximation argument of machine learning, breaki…