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CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds
Related publications (41)
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Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence.Althou ...
EPFL2024Meta-learning to address diverse Earth observation problems across resolutions
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Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...
London2024Robust machine learning for neuroscientific inference
Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...
EPFL2024Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU
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Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objecti ...
FRONTIERS MEDIA SA2022Textual Explanations and Critiques in Recommendation Systems
Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...
EPFL2022Instance norm improves meta-learning in class-imbalanced land cover classification
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Distribution shift is omnipresent in geographic data, where various climatic and cultural factors lead to different representations across the globe. We aim to adapt dynamically to unseen data distributions with model-agnostic meta-learning, where data sa ...
2022Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones
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In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers’ performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learnin ...
2022Lifelong Machine Learning with Data Efficiency and Knowledge Retention
Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...
EPFL2021Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging
Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...
OXFORD UNIV PRESS INC2021Population pharmacokinetic model selection assisted by machine learning
Jan Sickmann Hesthaven, Nadia Terranova
A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...
SPRINGER/PLENUM PUBLISHERS2021