2 papers
cs.LG2025
Meta-Learning at Scale for Large Language Models via Low-Rank Amortized Bayesian Meta-Learning
Liyi Zhang, Jake Snell, Thomas L. Griffiths
Fine-tuning large language models (LLMs) with low-rank adaptation (LoRA) is a cost-effective way to incorporate information from a specific dataset. However, when a problem require…
cs.LG2023
A Metalearned Neural Circuit for Nonparametric Bayesian Inference
Jake C. Snell, Gianluca Bencomo, Thomas L. Griffiths
Most applications of machine learning to classification assume a closed set of balanced classes. This is at odds with the real world, where class occurrence statistics often follow…