mt/thesis/bib/bibliography.bib

@article{anomaly_detection_survey,
	author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
	title = {Anomaly detection: A survey},
	year = {2009},
	issue_date = {July 2009},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	volume = {41},
	number = {3},
	issn = {0360-0300},
	url = {https://doi.org/10.1145/1541880.1541882},
	doi = {10.1145/1541880.1541882},
	abstract = {Anomaly detection is an important problem that has been researched
	            within diverse research areas and application domains. Many anomaly
	            detection techniques have been specifically developed for certain
	            application domains, while others are more generic. This survey
	            tries to provide a structured and comprehensive overview of the
	            research on anomaly detection. We have grouped existing techniques
	            into different categories based on the underlying approach adopted
	            by each technique. For each category we have identified key
	            assumptions, which are used by the techniques to differentiate
	            between normal and anomalous behavior. When applying a given
	            technique to a particular domain, these assumptions can be used as
	            guidelines to assess the effectiveness of the technique in that
	            domain. For each category, we provide a basic anomaly detection
	            technique, and then show how the different existing techniques in
	            that category are variants of the basic technique. This template
	            provides an easier and more succinct understanding of the
	            techniques belonging to each category. Further, for each category,
	            we identify the advantages and disadvantages of the techniques in
	            that category. We also provide a discussion on the computational
	            complexity of the techniques since it is an important issue in real
	            application domains. We hope that this survey will provide a better
	            understanding of the different directions in which research has
	            been done on this topic, and how techniques developed in one area
	            can be applied in domains for which they were not intended to begin
	            with.},
	journal = {ACM Comput. Surv.},
	month = jul,
	articleno = {15},
	numpages = {58},
	keywords = {outlier detection, Anomaly detection},
},
@dataset{alexander_kyuroson_2023_7913307,
	author = {Alexander Kyuroson and Niklas Dahlquist and Nikolaos Stathoulopoulos
	          and Vignesh Kottayam Viswanathan and Anton Koval and George
	          Nikolakopoulos},
	title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
	         Particles for Frontier Exploration },
	month = may,
	year = 2023,
	publisher = {Zenodo},
	version = {v1},
	doi = {10.5281/zenodo.7913307},
	url = {https://doi.org/10.5281/zenodo.7913307},
},
@article{deepsad,
	author = {Lukas Ruff and Robert A. Vandermeulen and Nico G{\"{o}}rnitz and
	          Alexander Binder and Emmanuel M{\"{u}}ller and Klaus{-}Robert M{\"{u}
	          }ller and Marius Kloft},
	title = {Deep Semi-Supervised Anomaly Detection},
	journal = {CoRR},
	volume = {abs/1906.02694},
	year = {2019},
	url = {http://arxiv.org/abs/1906.02694},
	eprinttype = {arXiv},
	eprint = {1906.02694},
	timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-1906-02694.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org},
},
@inproceedings{subter,
	title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
	         Particles for Frontier Exploration},
	url = {http://dx.doi.org/10.1109/MED59994.2023.10185906},
	DOI = {10.1109/med59994.2023.10185906},
	booktitle = {2023 31st Mediterranean Conference on Control and Automation
	             (MED)},
	publisher = {IEEE},
	author = {Kyuroson, Alexander and Dahlquist, Niklas and Stathoulopoulos,
	          Nikolaos and Viswanathan, Vignesh Kottayam and Koval, Anton and
	          Nikolakopoulos, George},
	year = {2023},
	month = jun,
	pages = {716–721},
}
,
@inproceedings{deepsvdd,
	title = {Deep One-Class Classification},
	author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
	          Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
	          , Emmanuel and Kloft, Marius},
	booktitle = {Proceedings of the 35th International Conference on Machine
	             Learning},
	pages = {4393--4402},
	year = {2018},
	editor = {Dy, Jennifer and Krause, Andreas},
	volume = {80},
	series = {Proceedings of Machine Learning Research},
	month = {10--15 Jul},
	publisher = {PMLR},
	pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
	url = {https://proceedings.mlr.press/v80/ruff18a.html},
	abstract = {Despite the great advances made by deep learning in many machine
	            learning problems, there is a relative dearth of deep learning
	            approaches for anomaly detection. Those approaches which do exist
	            involve networks trained to perform a task other than anomaly
	            detection, namely generative models or compression, which are in
	            turn adapted for use in anomaly detection; they are not trained on
	            an anomaly detection based objective. In this paper we introduce a
	            new anomaly detection method—Deep Support Vector Data Description—,
	            which is trained on an anomaly detection based objective. The
	            adaptation to the deep regime necessitates that our neural network
	            and training procedure satisfy certain properties, which we
	            demonstrate theoretically. We show the effectiveness of our method
	            on MNIST and CIFAR-10 image benchmark datasets as well as on the
	            detection of adversarial examples of GTSRB stop signs.},
},
@inproceedings{deep_svdd,
	title = {Deep One-Class Classification},
	author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
	          Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
	          , Emmanuel and Kloft, Marius},
	booktitle = {Proceedings of the 35th International Conference on Machine
	             Learning},
	pages = {4393--4402},
	year = {2018},
	editor = {Dy, Jennifer and Krause, Andreas},
	volume = {80},
	series = {Proceedings of Machine Learning Research},
	month = {10--15 Jul},
	publisher = {PMLR},
	pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
	url = {https://proceedings.mlr.press/v80/ruff18a.html},
	abstract = {Despite the great advances made by deep learning in many machine
	            learning problems, there is a relative dearth of deep learning
	            approaches for anomaly detection. Those approaches which do exist
	            involve networks trained to perform a task other than anomaly
	            detection, namely generative models or compression, which are in
	            turn adapted for use in anomaly detection; they are not trained on
	            an anomaly detection based objective. In this paper we introduce a
	            new anomaly detection method—Deep Support Vector Data Description—,
	            which is trained on an anomaly detection based objective. The
	            adaptation to the deep regime necessitates that our neural network
	            and training procedure satisfy certain properties, which we
	            demonstrate theoretically. We show the effectiveness of our method
	            on MNIST and CIFAR-10 image benchmark datasets as well as on the
	            detection of adversarial examples of GTSRB stop signs.},
},
  @inproceedings{anomaly_detection_medical,
	author = {{Wei}, Qi and {Ren}, Yinhao and {Hou}, Rui and {Shi}, Bibo and {Lo},
	          Joseph Y. and {Carin}, Lawrence},
	title = "{Anomaly detection for medical images based on a one-class
	         classification}",
	booktitle = {Medical Imaging 2018: Computer-Aided Diagnosis},
	year = 2018,
	editor = {{Petrick}, Nicholas and {Mori}, Kensaku},
	series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference
	          Series},
	volume = {10575},
	month = feb,
	eid = {105751M},
	pages = {105751M},
	doi = {10.1117/12.2293408},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2018SPIE10575E..1MW},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
},
  @article{anomaly_detection_defi,
	author = {Ul Hassan, Muneeb and Rehmani, Mubashir Husain and Chen, Jinjun},
	journal = {IEEE Communications Surveys \& Tutorials},
	title = {Anomaly Detection in Blockchain Networks: A Comprehensive Survey},
	year = {2023},
	volume = {25},
	number = {1},
	pages = {289-318},
	keywords = {Blockchains;Anomaly detection;Security;Smart
	            contracts;Privacy;Bitcoin;Tutorials;Blockchain;anomaly
	            detection;fraud detection},
	doi = {10.1109/COMST.2022.3205643},
}
,
  @article{anomaly_detection_manufacturing,
	AUTHOR = {Oh, Dong Yul and Yun, Il Dong},
	TITLE = {Residual Error Based Anomaly Detection Using Auto-Encoder in SMD
	         Machine Sound},
	JOURNAL = {Sensors},
	VOLUME = {18},
	YEAR = {2018},
	NUMBER = {5},
	ARTICLE-NUMBER = {1308},
	URL = {https://www.mdpi.com/1424-8220/18/5/1308},
	PubMedID = {29695084},
	ISSN = {1424-8220},
	ABSTRACT = {Detecting an anomaly or an abnormal situation from given noise is
	            highly useful in an environment where constantly verifying and
	            monitoring a machine is required. As deep learning algorithms are
	            further developed, current studies have focused on this problem.
	            However, there are too many variables to define anomalies, and the
	            human annotation for a large collection of abnormal data labeled at
	            the class-level is very labor-intensive. In this paper, we propose
	            to detect abnormal operation sounds or outliers in a very complex
	            machine along with reducing the data-driven annotation cost. The
	            architecture of the proposed model is based on an auto-encoder, and
	            it uses the residual error, which stands for its reconstruction
	            quality, to identify the anomaly. We assess our model using
	            Surface-Mounted Device (SMD) machine sound, which is very complex,
	            as experimental data, and state-of-the-art performance is
	            successfully achieved for anomaly detection.},
	DOI = {10.3390/s18051308},
},
  @article{anomaly_detection_history,
	author = {F.Y. Edgeworth and},
	title = {XLI. On discordant observations },
	journal = {The London, Edinburgh, and Dublin Philosophical Magazine and
	           Journal of Science},
	volume = {23},
	number = {143},
	pages = {364--375},
	year = {1887},
	publisher = {Taylor \& Francis},
	doi = {10.1080/14786448708628471},
	URL = { https://doi.org/10.1080/14786448708628471 },
	eprint = { https://doi.org/10.1080/14786448708628471 },
},
  @inproceedings{degradation_quantification_rain,
	author = {Zhang, Chen and Huang, Zefan and Ang, Marcelo H. and Rus, Daniela},
	booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and
	             Systems (IROS)},
	title = {LiDAR Degradation Quantification for Autonomous Driving in Rain},
	year = {2021},
	volume = {},
	number = {},
	pages = {3458-3464},
	keywords = {Degradation;Location awareness;Laser radar;Rain;Codes;System
	            performance;Current measurement},
	doi = {10.1109/IROS51168.2021.9636694},
},
  @article{deep_learning_overview,
	title = {Deep learning in neural networks: An overview},
	journal = {Neural Networks},
	volume = {61},
	pages = {85-117},
	year = {2015},
	issn = {0893-6080},
	doi = {https://doi.org/10.1016/j.neunet.2014.09.003},
	url = {https://www.sciencedirect.com/science/article/pii/S0893608014002135},
	author = {Jürgen Schmidhuber},
	keywords = {Deep learning, Supervised learning, Unsupervised learning,
	            Reinforcement learning, Evolutionary computation},
	abstract = {In recent years, deep artificial neural networks (including
	            recurrent ones) have won numerous contests in pattern recognition
	            and machine learning. This historical survey compactly summarizes
	            relevant work, much of it from the previous millennium. Shallow and
	            Deep Learners are distinguished by the depth of their credit
	            assignment paths, which are chains of possibly learnable, causal
	            links between actions and effects. I review deep supervised
	            learning (also recapitulating the history of backpropagation),
	            unsupervised learning, reinforcement learning & evolutionary
	            computation, and indirect search for short programs encoding deep
	            and large networks.},
},
@article{autoencoder_survey,
	title = {A comprehensive survey on design and application of autoencoder in
	         deep learning},
	journal = {Applied Soft Computing},
	volume = {138},
	pages = {110176},
	year = {2023},
	issn = {1568-4946},
	doi = {https://doi.org/10.1016/j.asoc.2023.110176},
	url = {https://www.sciencedirect.com/science/article/pii/S1568494623001941},
	author = {Pengzhi Li and Yan Pei and Jianqiang Li},
	keywords = {Deep learning, Autoencoder, Unsupervised learning, Feature
	            extraction, Autoencoder application},
	abstract = {Autoencoder is an unsupervised learning model, which can
	            automatically learn data features from a large number of samples
	            and can act as a dimensionality reduction method. With the
	            development of deep learning technology, autoencoder has attracted
	            the attention of many scholars. Researchers have proposed several
	            improved versions of autoencoder based on different application
	            fields. First, this paper explains the principle of a conventional
	            autoencoder and investigates the primary development process of an
	            autoencoder. Second, We proposed a taxonomy of autoencoders
	            according to their structures and principles. The related
	            autoencoder models are comprehensively analyzed and discussed. This
	            paper introduces the application progress of autoencoders in
	            different fields, such as image classification and natural language
	            processing, etc. Finally, the shortcomings of the current
	            autoencoder algorithm are summarized, and prospected for its future
	            development directions are addressed.},
}, 
@article{semi_overview,
	author = {Yang, Xiangli and Song, Zixing and King, Irwin and Xu, Zenglin},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	title = {A Survey on Deep Semi-Supervised Learning},
	year = {2023},
	volume = {35},
	number = {9},
	pages = {8934-8954},
	keywords = {Semisupervised learning;Training;Taxonomy;Task analysis;Deep
	            learning;Data models;Supervised learning;Deep semi-supervised
	            learning;semi-supervised learning;deep learning},
	doi = {10.1109/TKDE.2022.3220219},
}, 
@book{ai_fundamentals_book,
	title = {Fundamentals of Artificial Intelligence},
	url = {http://dx.doi.org/10.1007/978-81-322-3972-7},
	DOI = {10.1007/978-81-322-3972-7},
	publisher = {Springer India},
	author = {Chowdhary, K.R.},
	year = {2020},
	language = {en},
},

@article{machine_learning_overview,
	title = {Machine Learning from Theory to Algorithms: An Overview},
	volume = {1142},
	ISSN = {1742-6596},
	url = {http://dx.doi.org/10.1088/1742-6596/1142/1/012012},
	DOI = {10.1088/1742-6596/1142/1/012012},
	journal = {Journal of Physics: Conference Series},
	publisher = {IOP Publishing},
	author = {Alzubi, Jafar and Nayyar, Anand and Kumar, Akshi},
	year = {2018},
	month = nov,
	pages = {012012},
},

@article{machine_learning_first_definition,
	title = {Some Studies in Machine Learning Using the Game of Checkers},
	volume = {3},
	ISSN = {0018-8646},
	url = {http://dx.doi.org/10.1147/rd.33.0210},
	DOI = {10.1147/rd.33.0210},
	number = {3},
	journal = {IBM Journal of Research and Development},
	publisher = {IBM},
	author = {Samuel, A. L.},
	year = {1959},
	month = jul,
	pages = {210–229},
},
@inproceedings{bg_ad_pointclouds_scans,
	title = {Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors
	         },
	url = {http://dx.doi.org/10.1109/WACV56688.2023.00264},
	DOI = {10.1109/wacv56688.2023.00264},
	booktitle = {2023 IEEE/CVF Winter Conference on Applications of Computer
	             Vision (WACV)},
	publisher = {IEEE},
	author = {Bergmann, Paul and Sattlegger, David},
	year = {2023},
	month = jan,
	pages = {2612–2622},
},
  @article{bg_ad_pointclouds_poles,
	title = {Automatic Detection and Classification of Pole-Like Objects in Urban
	         Point Cloud Data Using an Anomaly Detection Algorithm},
	volume = {7},
	ISSN = {2072-4292},
	url = {http://dx.doi.org/10.3390/rs71012680},
	DOI = {10.3390/rs71012680},
	number = {10},
	journal = {Remote Sensing},
	publisher = {MDPI AG},
	author = {Rodríguez-Cuenca, Borja and García-Cortés, Silverio and Ordóñez,
	          Celestino and Alonso, Maria},
	year = {2015},
	month = sep,
	pages = {12680–12703},
},

@article{semi_ad_survey,
	title = {Semi-supervised anomaly detection algorithms: A comparative summary
	         and future research directions},
	volume = {218},
	ISSN = {0950-7051},
	url = {http://dx.doi.org/10.1016/j.knosys.2021.106878},
	DOI = {10.1016/j.knosys.2021.106878},
	journal = {Knowledge-Based Systems},
	publisher = {Elsevier BV},
	author = {Villa-Pérez, Miryam Elizabeth and Álvarez-Carmona, Miguel Á. and
	          Loyola-González, Octavio and Medina-Pérez, Miguel Angel and
	          Velazco-Rossell, Juan Carlos and Choo, Kim-Kwang Raymond},
	year = {2021},
	month = apr,
	pages = {106878},
},
@inbook{bg_autoencoder_ad,
	title = {Outlier Detection with Autoencoder Ensembles},
	ISBN = {9781611974973},
	url = {http://dx.doi.org/10.1137/1.9781611974973.11},
	DOI = {10.1137/1.9781611974973.11},
	booktitle = {Proceedings of the 2017 SIAM International Conference on Data
	             Mining},
	publisher = {Society for Industrial and Applied Mathematics},
	author = {Chen, Jinghui and Sathe, Saket and Aggarwal, Charu and Turaga,
	          Deepak},
	year = {2017},
	month = jun,
	pages = {90–98},
},
@inproceedings{bg_autoencoder_ad_2,
	title = {Memorizing Normality to Detect Anomaly: Memory-Augmented Deep
	         Autoencoder for Unsupervised Anomaly Detection},
	url = {http://dx.doi.org/10.1109/ICCV.2019.00179},
	DOI = {10.1109/iccv.2019.00179},
	booktitle = {2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
	publisher = {IEEE},
	author = {Gong, Dong and Liu, Lingqiao and Le, Vuong and Saha, Budhaditya and
	          Mansour, Moussa Reda and Venkatesh, Svetha and Van Den Hengel, Anton},
	year = {2019},
	month = oct,
	pages = {1705–1714},
},
@article{bg_autoencoder_lidar,
	title = {Deep Learning Approach for Building Detection Using LiDAR–Orthophoto
	         Fusion},
	volume = {2018},
	ISSN = {1687-7268},
	url = {http://dx.doi.org/10.1155/2018/7212307},
	DOI = {10.1155/2018/7212307},
	journal = {Journal of Sensors},
	publisher = {Wiley},
	author = {Nahhas, Faten Hamed and Shafri, Helmi Z. M. and Sameen, Maher
	          Ibrahim and Pradhan, Biswajeet and Mansor, Shattri},
	year = {2018},
	month = aug,
	pages = {1–12},
},
@article{lidar_denoising_survey,
	title = {LiDAR Denoising Methods in Adverse Environments: A Review},
	volume = {25},
	ISSN = {2379-9153},
	url = {http://dx.doi.org/10.1109/JSEN.2025.3526175},
	DOI = {10.1109/jsen.2025.3526175},
	number = {5},
	journal = {IEEE Sensors Journal},
	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
	author = {Park, Ji-Il and Jo, SeungHyeon and Seo, Hyung-Tae and Park, Jihyuk},
	year = {2025},
	month = mar,
	pages = {7916–7932},
},
@inproceedings{lidar_subt_dust_removal,
	title = {Efficient Real-time Smoke Filtration with 3D LiDAR for Search and
	         Rescue with Autonomous Heterogeneous Robotic Systems},
	url = {http://dx.doi.org/10.1109/IECON51785.2023.10312303},
	DOI = {10.1109/iecon51785.2023.10312303},
	booktitle = {IECON 2023- 49th Annual Conference of the IEEE Industrial
	             Electronics Society},
	publisher = {IEEE},
	author = {Kyuroson, Alexander and Koval, Anton and Nikolakopoulos, George},
	year = {2023},
	month = oct,
	pages = {1–7},
},
@article{lidar_denoising_dust,
	title = {Dust De-Filtering in LiDAR Applications With Conventional and CNN
	         Filtering Methods},
	volume = {12},
	ISSN = {2169-3536},
	url = {http://dx.doi.org/10.1109/ACCESS.2024.3362804},
	DOI = {10.1109/access.2024.3362804},
	journal = {IEEE Access},
	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
	author = {Parsons, Tyler and Seo, Jaho and Kim, Byeongjin and Lee, Hanmin and
	          Kim, Ji-Chul and Cha, Moohyun},
	year = {2024},
	pages = {22032–22042},
},
  @inproceedings{lidar_errormodel_particles,
	author = {Mokrane, Hadj-Bachir and De Souza, Philippe and Nordqvist, P and Roy
	          , N},
	year = {2021},
	month = {05},
	pages = {},
	title = {Modelling of LIDAR sensor disturbances by solid airborne particles},
},
@article{lidar_errormodel_automotive,
	title = {A Survey on Sensor Failures in Autonomous Vehicles: Challenges and
	         Solutions},
	volume = {24},
	ISSN = {1424-8220},
	url = {http://dx.doi.org/10.3390/s24165108},
	DOI = {10.3390/s24165108},
	number = {16},
	journal = {Sensors},
	publisher = {MDPI AG},
	author = {Matos, Francisco and Bernardino, Jorge and Durães, João and Cunha,
	          João},
	year = {2024},
	month = aug,
	pages = {5108},
},
@article{lidar_errormodel_consensus,
	title = {Present and Future of SLAM in Extreme Environments: The DARPA SubT
	         Challenge},
	volume = {40},
	ISSN = {1941-0468},
	url = {http://dx.doi.org/10.1109/TRO.2023.3323938},
	DOI = {10.1109/tro.2023.3323938},
	journal = {IEEE Transactions on Robotics},
	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
	author = {Ebadi, Kamak and Bernreiter, Lukas and Biggie, Harel and Catt, Gavin
	          and Chang, Yun and Chatterjee, Arghya and Denniston, Christopher E.
	          and Desch\^enes, Simon-Pierre and Harlow, Kyle and Khattak, Shehryar
	          and Nogueira, Lucas and Palieri, Matteo and Petráček, Pavel and
	          Petrlík, Matěj and Reinke, Andrzej and Krátký, Vít and Zhao, Shibo
	          and Agha-mohammadi, Ali-akbar and Alexis, Kostas and Heckman,
	          Christoffer and Khosoussi, Kasra and Kottege, Navinda and Morrell,
	          Benjamin and Hutter, Marco and Pauling, Fred and Pomerleau, Fran\c{c}
	          ois and Saska, Martin and Scherer, Sebastian and Siegwart, Roland and
	          Williams, Jason L. and Carlone, Luca},
	year = {2024},
	pages = {936–959},
},
@article{when_the_dust_settles,
	title = {When the Dust Settles: The Four Behaviors of LiDAR in the Presence of
	         Fine Airborne Particulates},
	volume = {34},
	ISSN = {1556-4967},
	url = {http://dx.doi.org/10.1002/rob.21701},
	DOI = {10.1002/rob.21701},
	number = {5},
	journal = {Journal of Field Robotics},
	publisher = {Wiley},
	author = {Phillips, Tyson Govan and Guenther, Nicky and McAree, Peter Ross},
	year = {2017},
	month = feb,
	pages = {985–1009},
},
@misc{odds,
	author = {Shebuti Rayana},
	year = {2016},
	title = {ODDS Library},
	url = {https://odds.cs.stonybrook.edu},
	institution = {Stony Brook University, Department of Computer Sciences},
},
@article{lenet,
	title = {Gradient-based learning applied to document recognition},
	volume = {86},
	ISSN = {0018-9219},
	url = {http://dx.doi.org/10.1109/5.726791},
	DOI = {10.1109/5.726791},
	number = {11},
	journal = {Proceedings of the IEEE},
	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
	author = {Lecun, Y. and Bottou, L. and Bengio, Y. and Haffner, P.},
	year = {1998},
	pages = {2278–2324},
},
@article{ef_concept_source,
	title = {Multi-Year ENSO Forecasts Using Parallel Convolutional Neural
	         Networks With Heterogeneous Architecture},
	volume = {8},
	ISSN = {2296-7745},
	url = {http://dx.doi.org/10.3389/fmars.2021.717184},
	DOI = {10.3389/fmars.2021.717184},
	journal = {Frontiers in Marine Science},
	publisher = {Frontiers Media SA},
	author = {Ye, Min and Nie, Jie and Liu, Anan and Wang, Zhigang and Huang, Lei
	          and Tian, Hao and Song, Dehai and Wei, Zhiqiang},
	year = {2021},
	month = aug,
}, 
@article{ml_supervised_unsupervised_figure_source,
	title = {Virtual reality in biology: could we become virtual naturalists?},
	volume = {14},
	ISSN = {1936-6434},
	url = {http://dx.doi.org/10.1186/s12052-021-00147-x},
	DOI = {10.1186/s12052-021-00147-x},
	number = {1},
	journal = {Evolution: Education and Outreach},
	publisher = {Springer Science and Business Media LLC},
	author = {Morimoto, Juliano and Ponton, Fleur},
	year = {2021},
	month = may,
},
@article{ml_autoencoder_figure_source,
	title = "From Autoencoder to Beta-VAE",
	author = "Weng, Lilian",
	journal = "lilianweng.github.io",
	year = "2018",
	url = "https://lilianweng.github.io/posts/2018-08-12-vae/",
},

@conference{bg_lidar_figure_source,
	title = "1D MEMS Micro-Scanning LiDAR",
	author = "Norbert Druml and Ievgeniia Maksymova and Thomas Thurner and Lierop,
	          {D. van} and Hennecke, {Marcus E.} and Andreas Foroutan",
	year = "2018",
	month = sep,
	day = "16",
	language = "English",
}