10 citations · 14 across the 9 of their papers we have counts for
16 papers
Mosaic Learning: A Framework for Decentralized Learning with Model Fragmentation
Sayan Biswas, Davide Frey, Romaric Gaudel +7
Decentralized learning (DL) enables collaborative machine learning (ML) without a central server, making it suitable for settings where training data cannot be centrally hosted. We…
Optimizing Agentic Workflows using Meta-tools
Sami Abuzakuk, Anne-Marie Kermarrec, Rishi Sharma +2
Agentic AI enables LLM to dynamically reason, plan, and interact with tools to solve complex tasks. However, agentic workflows often require many iterative reasoning steps and tool…
Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster
Marie Reinbigler, Rishi Sharma, Rafael Pires +3
Analyzing gigapixel images is recognized as computationally demanding. In this paper, we introduce PyramidAI, a technique for analyzing gigapixel images with reduced computational…
HarMoEny: Efficient Multi-GPU Inference of MoE Models
Zachary Doucet, Rishi Sharma, Martijn de Vos +3
Mixture-of-Experts (MoE) models offer computational efficiency during inference by activating only a subset of specialized experts for a given input. This enables efficient model s…
Position: Collaborative Agentic AI Needs Interoperability Across Ecosystems
Rishi Sharma, Martijn de Vos, Pradyumna Chari +2
Collaborative agentic AI is projected to transform entire industries by enabling AI-powered agents to autonomously perceive, plan, and act within digital environments. Yet, current…
Revisiting Ensembling in One-Shot Federated Learning
Youssef Allouah, Akash Dhasade, Rachid Guerraoui +5
Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a signi…