Publication
Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection
Publications associées (51)
Can 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 ...
EPFL2024Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction
Martin Weigert, Benjamin Tobias Gallusser, Max Stieber
State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...
Cham2023Traversing Time Dependent Light Fields for Daylight Glare Evaluation
To understand how daylight gives shape and life to architectural spaces, whether existing or imagined, requires quantifying its dynamism and energy. Maintaining these details presents a challenge to simulation and analysis methods that flatten data into di ...
EPFL2023A Study on Gradient-based Meta-learning for Robust Deep Digital Twins
Olga Fink, Raffael Pascal Theiler, Michele Viscione
Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...
Research Publishing2023Robustness of Local Predictions in Atomistic Machine Learning Models
Michele Ceriotti, Federico Grasselli, Sanggyu Chong, Chiheb Ben Mahmoud
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...
Washington2023Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation
Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...
EPFL2022Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones
David Atienza Alonso, Dario Floreano, Ricardo Andres Chavarriaga Lozano, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Ping-Keng Jao
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 ...
2022Instance norm improves meta-learning in class-imbalanced land cover classification
Devis Tuia, Marc Conrad Russwurm
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 ...
2022Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover
Devis Tuia, Marc Conrad Russwurm
Learning to predict accurately from a few data samples is a central challenge in modern data-hungry machine learning. On natural images, human vision typically outperforms deep learning approaches on few-shot learning. However, we hypothesize that aerial a ...
IEEE2022