22 citations · 35 across the 7 of their papers we have counts for
4 papers · 1 filter
Exploring How LLMs Capture and Represent Domain-Specific Knowledge
Mirian Hipolito Garcia, Camille Couturier, Daniel Madrigal Diaz +5
We study whether Large Language Models (LLMs) inherently capture domain-specific nuances in natural language. Our experiments probe the domain sensitivity of LLMs by examining thei…
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings
Yehya Farhat, Hamza ElMokhtar Shili, Fangshuo Liao +7
Mixture-of-Experts (MoEs) achieve scalability by dynamically activating subsets of their components. Yet, understanding how expertise emerges through joint training of gating mecha…
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Chen Dun, Mirian Hipolito, Chris Jermaine +2
Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup, where slower clients can severely impede the learning process. Here…
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Mirian Hipolito Garcia, Andre Manoel, Daniel Madrigal Diaz +3
In this paper we introduce "Federated Learning Utilities and Tools for Experimentation" (FLUTE), a high-performance open-source platform for federated learning research and offline…