GitHub

Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.

The main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To get the best ...
IEEE

Low-Latency and Energy-Efficient Federated Learning Over Cell-Free Networks: A Trade-Off Analysis

Abstract: Federated learning (FL) enables distributed model training by exchanging models rather than raw data, preserving privacy and reducing communication overhead. However, as the number of FL ...
IEEE

Learning Positive–Negative Prompts for Open-Set Remote Sensing Scene Classification

Abstract: Most remote sensing scene classification (RSSC) methods are primarily built upon the closed-set assumption, which assumes that all test samples definitely belong to one of the categories ...