335 citations · 882 across the 15 of their papers we have counts for
4 papers · 1 filter
Large Language Models Encode Clinical Knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu +27
Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications i…
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets
Raviteja Vemulapalli, Warren Richard Morningstar, Philip Andrew Mansfield +4
Dual encoding models that encode a pair of inputs are widely used for representation learning. Many approaches train dual encoding models by maximizing agreement between pairs of e…
Plex: Towards Reliability using Pretrained Large Model Extensions
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry +23
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. P…
Mixed Federated Learning: Joint Decentralized and Centralized Learning
Sean Augenstein, Andrew Hard, Lin Ning +4
Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixe…