Zobrazeno 1 - 10
of 226
pro vyhledávání: '"Kocev, Dragi"'
Publikováno v:
IEEE Geoscience and Remote Sensing Letters (2024)
We investigate the utility of in-domain self-supervised pre-training of vision models in the analysis of remote sensing imagery. Self-supervised learning (SSL) has emerged as a promising approach for remote sensing image classification due to its abi
Externí odkaz:
http://arxiv.org/abs/2307.01645
Autor:
Kostovska, Ana, Bogatinovski, Jasmin, Treven, Andrej, Džeroski, Sašo, Kocev, Dragi, Panov, Panče
The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and methods that appear in the literature. Hence, ensuring proper, correct, robu
Externí odkaz:
http://arxiv.org/abs/2211.12757
Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest in different application domains such as text mining, computer vision,
Externí odkaz:
http://arxiv.org/abs/2211.11227
The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Di
Externí odkaz:
http://arxiv.org/abs/2208.03163
Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled examples, but also unlabeled examples. While SSL for the simple tasks of classification and regression has received a lot of attention from the r
Externí odkaz:
http://arxiv.org/abs/2207.09237
We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more tha
Externí odkaz:
http://arxiv.org/abs/2207.07189
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datase
Externí odkaz:
http://arxiv.org/abs/2201.08789
Autor:
Petković, Matej, Lucas, Luke, Stepišnik, Tomaž, Panov, Panče, Simidjievski, Nikola, Kocev, Dragi
The Mars Express (MEX) spacecraft has been orbiting Mars since 2004. The operators need to constantly monitor its behavior and handle sporadic deviations (outliers) from the expected patterns of measurements of quantities that the satellite is sendin
Externí odkaz:
http://arxiv.org/abs/2108.02067
Autor:
Kostovska, Ana, Petković, Matej, Stepišnik, Tomaž, Lucas, Luke, Finn, Timothy, Martínez-Heras, José, Panov, Panče, Džeroski, Sašo, Donati, Alessandro, Simidjievski, Nikola, Kocev, Dragi
Publikováno v:
8th IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT 2021)
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning algorithms for multivariate time series analyses, classification, reg
Externí odkaz:
http://arxiv.org/abs/2108.01407