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Machine learning
Related publications (1,000)
Meta-learning to address diverse Earth observation problems across resolutions
Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm
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 ...
London2024Topics in statistical physics of high-dimensional machine learning
In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...
EPFL2024Robust 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 ...
EPFL2024Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection
Supervised machine learning models are receiving increasing attention in electricity theft detection due to their high detection accuracy. However, their performance depends on a massive amount of labeled training data, which comes from time-consuming and ...
Piscataway2024Can Gas Consumption Data Improve the Performance of Electricity Theft Detection?
Machine learning techniques have been extensively developed in the field of electricity theft detection. However, almost all typical models primarily rely on electricity consumption data to identify fraudulent users, often neglecting other pertinent househ ...
Ieee-Inst Electrical Electronics Engineers Inc2024Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation
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 ...
EPFL2024Random matrix methods for high-dimensional machine learning models
In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...
EPFL2024BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
David Atienza Alonso, Miguel Peon Quiros, Pasquale Davide Schiavone, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu, Dimitrios Samakovlis, Stefano Albini
The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despit ...
2024CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds
David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Ali Pahlevan
Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...
2024Thermal transport of glasses via machine learning driven simulations
Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...
Lausanne2024