move typst to root and delte latex

sources.bib

@@ -0,0 +1,110 @@
+%! Author = lukas
+%! Date = 4/9/24
+
+@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}
+}
+
+@misc{efficientADpaper,
+    title={EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies},
+    author={Kilian Batzner and Lars Heckler and Rebecca König},
+    year={2024},
+    eprint={2303.14535},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV},
+    url={https://arxiv.org/abs/2303.14535},
+}
+
+@misc{patchcorepaper,
+    title={Towards Total Recall in Industrial Anomaly Detection},
+    author={Karsten Roth and Latha Pemula and Joaquin Zepeda and Bernhard Schölkopf and Thomas Brox and Peter Gehler},
+    year={2022},
+    eprint={2106.08265},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV},
+    url={https://arxiv.org/abs/2106.08265},
+}
+
+@misc{jupyter,
+	    author = {},
+	    title = {{Project Jupyter Documentation}},
+	    howpublished = "\url{https://docs.jupyter.org/en/latest/}",
+	    year = {2024},
+	    note = "[Online; accessed 13-May-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}
+}
+
+@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{datasetsampleimg,
+    author = {},
+    title = {{The MVTec anomaly detection dataset (MVTec AD)}},
+    howpublished = "\url{https://www.mvtec.com/company/research/datasets/mvtec-ad}",
+    year = {2024},
+    note = "[Online; accessed 12-April-2024]"
+}
+
+@inproceedings{liang2017soft,
+    title={Soft-margin softmax for deep classification},
+    author={Liang, Xuezhi and Wang, Xiaobo and Lei, Zhen and Liao, Shengcai and Li, Stan Z},
+    booktitle={International Conference on Neural Information Processing},
+    pages={413--421},
+    year={2017},
+    organization={Springer}
+}
+
+@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{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{snell2017prototypicalnetworksfewshotlearning,
+      title={Prototypical Networks for Few-shot Learning},
+      author={Jake Snell and Kevin Swersky and Richard S. Zemel},
+      year={2017},
+      eprint={1703.05175},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1703.05175},
+}