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Found 13 papers, 4 papers with code
Federated Variational Inference: Towards Improved Personalization and Generalization
We then approximate this process using Variational Inference to train our model efficiently.
Towards Expert-Level Medical Question Answering with Large Language Models
Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67. 2% on the MedQA dataset.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #3 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets
In this work, we focus on federated training of dual encoding models on decentralized data composed of many small, non-IID (independent and identically distributed) client datasets.
Plex: Towards Reliability using Pretrained Large Model Extensions
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.
Mixed Federated Learning: Joint Decentralized and Centralized Learning
For example, additional datacenter data can be leveraged to jointly learn from centralized (datacenter) and decentralized (federated) training data and better match an expected inference data distribution.
Solving it correctly Prevalence and Persistence of Gender Gap in Basic Mathematics in rural India
Mathematical ability is among the most important determinants of prospering in the labour market.
What Do We Mean by Generalization in Federated Learning?
Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps from unseen client distributions (participation gap).
Learning Federated Representations and Recommendations with Limited Negatives
Deep retrieval models are widely used for learning entity representations and recommendations.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
Federated Reconstruction: Partially Local Federated Learning
We also describe the successful deployment of this approach at scale for federated collaborative filtering in a mobile keyboard application.
Presence of Women in Economics Academia: Evidence from India
This paper documents the representation of women in Economics academia in India by analyzing the share of women in faculty positions, and their participation in a prestigious conference held annually.
Learning Multilingual Word Embeddings Using Image-Text Data
There has been significant interest recently in learning multilingual word embeddings -- in which semantically similar words across languages have similar embeddings.
