1 citations · 1 across the 3 of their papers we have counts for
3 papers
BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
Jerry Liu, Yasa Baig, Denise Hui Jean Lee +3
Physics-informed neural networks (PINNs) offer a flexible way to solve partial differential equations (PDEs) with machine learning, yet they still fall well short of the machine-pr…
Cartridges: Lightweight and general-purpose long context representations via self-study
Sabri Eyuboglu, Ryan Ehrlich, Simran Arora +8
Large language models are often used to answer queries grounded in large text corpora (e.g. codebases, legal documents, or chat histories) by placing the entire corpus in the conte…
Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models
Avanika Narayan, Dan Biderman, Sabri Eyuboglu +4
We investigate an emerging setup in which a small, on-device language model (LM) with access to local data communicates with a frontier, cloud-hosted LM to solve real-world tasks i…