2 papers
cs.LG2025
Compressed Concatenation of Small Embedding Models
Mohamed Ayoub Ben Ayad, Michael Dinzinger, Kanishka Ghosh Dastidar +2
Embedding models are central to dense retrieval, semantic search, and recommendation systems, but their size often makes them impractical to deploy in resource-constrained environm…
cs.LG2024
SUDS: A Strategy for Unsupervised Drift Sampling
Christofer Fellicious, Lorenz Wendlinger, Mario Gancarski +2
Supervised machine learning often encounters concept drift, where the data distribution changes over time, degrading model performance. Existing drift detection methods focus on id…