60 citations · 94 across the 11 of their papers we have counts for
12 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…
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…
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…
Serverless Cold Starts and Where to Find Them
Artjom Joosen, Ahmed Hassan, Martin Asenov +5
This paper releases and analyzes a month-long trace of 85 billion user requests and 11.9 million cold starts from Huawei's serverless cloud platform. Our analysis spans workloads f…
DAM: Towards A Foundation Model for Time Series Forecasting
Luke Darlow, Qiwen Deng, Ahmed Hassan +5
It is challenging to scale time series forecasting models such that they forecast accurately for multiple distinct domains and datasets, all with potentially different underlying c…
How Does It Function? Characterizing Long-term Trends in Production Serverless Workloads
Artjom Joosen, Ahmed Hassan, Martin Asenov +4
This paper releases and analyzes two new Huawei cloud serverless traces. The traces span a period of over 7 months with over 1.4 trillion function invocations combined. The first t…
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…
Vision-based system identification and 3D keypoint discovery using dynamics constraints
Miguel Jaques, Martin Asenov, Michael Burke +1
This paper introduces V-SysId, a novel method that enables simultaneous keypoint discovery, 3D system identification, and extrinsic camera calibration from an unlabeled video taken…
Vid2Param: Modelling of Dynamics Parameters from Video
Martin Asenov, Michael Burke, Daniel Angelov +3
Videos provide a rich source of information, but it is generally hard to extract dynamical parameters of interest. Inferring those parameters from a video stream would be beneficia…
Active Localization of Gas Leaks using Fluid Simulation
Martin Asenov, Marius Rutkauskas, Derryck Reid +2
Sensors are routinely mounted on robots to acquire various forms of measurements in spatio-temporal fields. Locating features within these fields and reconstruction (mapping) of th…