[HTML][HTML] An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem
T Todic, V Stankovic, L Stankovic - Applied Energy, 2023 - Elsevier
With the widespread deployment of smart meters worldwide, quantification of energy used
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy
in the field of computer vision, harnessing the capabilities of machine learning (ML) to …
in the field of computer vision, harnessing the capabilities of machine learning (ML) to …
A survey on autonomous driving datasets: Data statistic, annotation, and outlook
Autonomous driving has rapidly developed and shown promising performance with recent
advances in hardware and deep learning methods. High-quality datasets are fundamental …
advances in hardware and deep learning methods. High-quality datasets are fundamental …
A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook
Autonomous driving has rapidly developed and shown promising performance due to recent
advances in hardware and deep learning techniques. High-quality datasets are fundamental …
advances in hardware and deep learning techniques. High-quality datasets are fundamental …
A unified active learning framework for annotating graph data with application to software source code performance prediction
Most machine learning and data analytics applications, including performance engineering
in software systems, require a large number of annotations and labelled data, which might …
in software systems, require a large number of annotations and labelled data, which might …
[HTML][HTML] A unified active learning framework for annotating graph data for regression task
In many domains, effectively applying machine learning models requires a large number of
annotations and labelled data, which might not be available in advance. Acquiring …
annotations and labelled data, which might not be available in advance. Acquiring …
Batch Mode Deep Active Learning for Regression on Graph Data
Acquiring labelled data for machine learning tasks, for example, for software performance
prediction, remains a resource-intensive task. This study extends our previous work by …
prediction, remains a resource-intensive task. This study extends our previous work by …
Passive and active learning of driver behavior from electric vehicles
F Comuni, C Mészáros, N Åkerblom… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Modeling driver behavior provides several advantages in the automotive industry, including
prediction of electric vehicle energy consumption. Studies have shown that aggressive …
prediction of electric vehicle energy consumption. Studies have shown that aggressive …
Effective Acquisition Functions for Active Correlation Clustering
L Aronsson, MH Chehreghani - arXiv preprint arXiv:2402.03587, 2024 - arxiv.org
Correlation clustering is a powerful unsupervised learning paradigm that supports positive
and negative similarities. In this paper, we assume the similarities are not known in advance …
and negative similarities. In this paper, we assume the similarities are not known in advance …
[PDF][PDF] Introduction to the special issue on Intelligent Control and Optimisation
Since the development of the first neuron model by Warren McCulloch and Walter Pitts in
1943 we have seen huge advances in AI and Machine Learning, from the Rosenblatt …
1943 we have seen huge advances in AI and Machine Learning, from the Rosenblatt …