%! Author = lukas
%! Date = 4/9/24

@InProceedings{Xu_2022_CVPR,
    author = {Xu, Yinghao and Wei, Fangyun and Sun, Xiao and Yang, Ceyuan and Shen, Yujun and Dai, Bo and Zhou, Bolei and Lin, Stephen},
    title = {Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2022},
    pages = {2959-2968}
}

@online{fixmatch,
    author = "Kihyuk Sohn, David Berthelot, Chun-Liang Li",
    title = "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence",
    url = "https://arxiv.org/abs/2001.07685",
    addendum = "(accessed: 20.03.2023)",
    keywords = "FixMatch, semi-supervised"
}

@InProceedings{activelearning,
    author = "Faria, Bruno
and Perdig{\~a}o, Dylan
and Br{\'a}s, Joana
and Macedo, Luis",
    editor = "Marreiros, Goreti
and Martins, Bruno
and Paiva, Ana
and Ribeiro, Bernardete
and Sardinha, Alberto",
    title = "The Joint Role of Batch Size and Query Strategy in Active Learning-Based Prediction - A Case Study in the Heart Attack Domain",
    booktitle = "Progress in Artificial Intelligence",
    year = "2022",
    publisher = "Springer International Publishing",
    address = "Cham",
    pages = "464--475",
    abstract = "This paper proposes an Active Learning algorithm that could detect heart attacks based on different body measures, which requires much less data than the passive learning counterpart while maintaining similar accuracy. To that end, different parameters were tested, namely the batch size and the query strategy used. The initial tests on batch size consisted of varying its value until 50. From these experiments, the conclusion was that the best results were obtained with lower values, which led to the second set of experiments, varying the batch size between 1 and 5 to understand in which value the accuracy was higher. Four query strategies were tested: random sampling, least confident sampling, margin sampling and entropy sampling. The results of each approach were similar, reducing by 57{\%} to 60{\%} the amount of data required to obtain the same results of the passive learning approach.",
    isbn = "978-3-031-16474-3"
}

@inbook{Boltzmann,
    place = {Cambridge},
    series = {Cambridge Library Collection - Physical  Sciences},
    title = {Studien über das Gleichgewicht der lebendigen Kraft zwischen bewegten materiellen Punkten},
    booktitle = {Wissenschaftliche Abhandlungen},
    publisher = {Cambridge University Press},
    author = {Boltzmann, Ludwig},
    editor = {Hasenöhrl, FriedrichEditor},
    year = {2012},
    pages = {49–96},
    collection = {Cambridge Library Collection - Physical  Sciences}, key = {value},}

@misc{dagster,
    author = {},
    title = {{Dagster getting started}},
    howpublished = "\url{https://docs.dagster.io/getting-started}",
    year = {2024},
    note = "[Online; accessed 12-April-2024]"
}

@misc{labelstudio,
    author = {},
    title = {{Label Studio Documentation}},
    howpublished = "\url{https://labelstud.io/guide/}",
    year = {2024},
    note = "[Online; accessed 13-May-2024]"
}

@misc{jupyter,
    author = {},
    title = {{Project Jupyter Documentation}},
    howpublished = "\url{https://docs.jupyter.org/en/latest/}",
    year = {2024},
    note = "[Online; accessed 13-May-2024]"
}

@misc{muffinsvschiuahuakaggle,
    author = {},
    title = {{Muffin vs Chihuahua Kaggle Dataset}},
    howpublished = "\url{https://www.kaggle.com/datasets/samuelcortinhas/muffin-vs-chihuahua-image-classification/data}",
    year = {2024},
    note = "[Online; accessed 12-April-2024]"
}

@misc{muffinsvschiuahuakaggle_poster,
    author = {},
    title = {{Muffin vs Chihuahua Kaggle Dataset Poster Image}},
    howpublished = "\url{https://i.postimg.cc/2SXNWP7f/muffin-meme2.jpg}",
    year = {2024},
    note = "[Online; accessed 12-April-2024]"
}

@INCOLLECTION{RubensRecSysHB2010,
author = {Neil Rubens and Dain Kaplan and Masashi Sugiyama},
title = {Active Learning in Recommender Systems},
booktitle = {Recommender Systems Handbook},
publisher = {Springer},
year = {2011},
editor = {P.B. Kantor and F. Ricci and L. Rokach and B. Shapira},
pages = {735-767},
doi = {10.1007/978-0-387-85820-3_23}
}

@techreport{settles.tr09,
    Author = {Burr Settles},
    Institution = {University of Wisconsin--Madison},
    Number = {1648},
    Title = {Active Learning Literature Survey},
    Type = {Computer Sciences Technical Report},
    Year = {2009},
}

@misc{generalAI,
    author        = {Johannes Brandstetter},
    title         = {Lecture notes in Theoretical Concepts of Machine Learning},
    month         = {May},
    year          = {2024},
    publisher={Johannes Kepler Universität Linz}
}

@misc{suptechniques,
    author        = {Andreas Radler, Markus Holzleitner},
    title         = {Lecture notes in Machine Learning: Supervised Techniques},
    month         = {September},
    year          = {2022},
    publisher={Johannes Kepler Universität Linz}
}

@online{ROCWikipedia,
    author  = "Wikimedia Commons",
    title   = "Receiver operating characteristic",
    year    = "2024",
    urlseen = "13-05-24",
    url     = "https://commons.wikimedia.org/wiki/File:Roc_curve.svg",
    note    = "File: \ttfamily{Roc curve.svg}",
}

@misc{resnet,
    title={Deep Residual Learning for Image Recognition},
    author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
    year={2015},
    eprint={1512.03385},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@misc{cnnarchitectureimg,
    author = {},
    title = {{What are convolutional neural networks?}},
    howpublished = "\url{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}",
    year = {2024},
    note = "[Online; accessed 12-April-2024]"
}

@misc{cnnintro,
    title={An Introduction to Convolutional Neural Networks},
    author={Keiron O'Shea and Ryan Nash},
    year={2015},
    eprint={1511.08458},
    archivePrefix={arXiv},
    primaryClass={cs.NE}
}

@InProceedings{crossentropy,
    ISSN = {00359246},
    URL = {http://www.jstor.org/stable/2984087},
    abstract = {This paper deals first with the relationship between the theory of probability and the theory of rational behaviour. A method is then suggested for encouraging people to make accurate probability estimates, a connection with the theory of information being mentioned. Finally Wald's theory of statistical decision functions is summarised and generalised and its relation to the theory of rational behaviour is discussed.},
    author = {I. J. Good},
    journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
    number = {1},
    pages = {107--114},
    publisher = {[Royal Statistical Society, Wiley]},
    title = {Rational Decisions},
    urldate = {2024-05-23},
    volume = {14},
    year = {1952}
}
