8 papers
Learning to Score: Tuning Cluster Schedulers through Reinforcement Learning
Martin Asenov, Qiwen Deng, Gingfung Yeung +1
Efficiently allocating incoming jobs to nodes in large-scale clusters can lead to substantial improvements in both cluster utilization and job performance. In order to allocate inc…
DISCO: Document Intelligence Suite for COmparative Evaluation
Kenza Benkirane, Dan Goldwater, Martin Asenov +1
Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for COmparati…
Retrieval or Representation? Reassessing Benchmark Gaps in Multilingual and Visually Rich RAG
Martin Asenov, Kenza Benkirane, Dan Goldwater +1
Retrieval-augmented generation (RAG) is a common way to ground language models in external documents and up-to-date information. Classical retrieval systems relied on lexical metho…
Lightweight Online Adaption for Time Series Foundation Model Forecasts
Thomas L. Lee, William Toner, Rajkarn Singh +2
Foundation models (FMs) have emerged as a promising approach for time series forecasting. While effective, FMs typically remain fixed during deployment due to the high computationa…
Performance of Zero-Shot Time Series Foundation Models on Cloud Data
William Toner, Thomas L. Lee, Artjom Joosen +2
Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. FMs are trained on numerous diverse datasets and claim to be effectiv…
Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics
Carlo Tiseo, Quentin Rouxel, Martin Asenov +4
Medical robotics can help improve and extend the reach of healthcare services. A major challenge for medical robots is the complex physical interaction between the robot and the pa…