[{"publisher":"AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany","date_updated":"2024-03-25T11:05:53Z","author":[{"id":"47565","full_name":"Gräßler, Iris","orcid":"0000-0001-5765-971X","last_name":"Gräßler","first_name":"Iris"},{"full_name":"Hieb, Michael","id":"72252","last_name":"Hieb","first_name":"Michael"}],"date_created":"2024-03-25T10:16:24Z","title":"Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing","doi":"10.5162/smsi2023/d7.4","conference":{"start_date":"2023-05-08","name":"SMSI 2023. Sensor and Measurement Science International","location":"Nuremberg","end_date":"2023-05-11"},"quality_controlled":"1","publication_status":"published","year":"2023","page":"253-524","citation":{"ama":"Gräßler I, Hieb M. Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing. In: <i>Lectures</i>. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany; 2023:253-524. doi:<a href=\"https://doi.org/10.5162/smsi2023/d7.4\">10.5162/smsi2023/d7.4</a>","ieee":"I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing,” in <i>Lectures</i>, Nuremberg, 2023, pp. 253–524, doi: <a href=\"https://doi.org/10.5162/smsi2023/d7.4\">10.5162/smsi2023/d7.4</a>.","chicago":"Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing.” In <i>Lectures</i>, 253–524. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023. <a href=\"https://doi.org/10.5162/smsi2023/d7.4\">https://doi.org/10.5162/smsi2023/d7.4</a>.","apa":"Gräßler, I., &#38; Hieb, M. (2023). Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing. <i>Lectures</i>, 253–524. <a href=\"https://doi.org/10.5162/smsi2023/d7.4\">https://doi.org/10.5162/smsi2023/d7.4</a>","mla":"Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing.” <i>Lectures</i>, AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp. 253–524, doi:<a href=\"https://doi.org/10.5162/smsi2023/d7.4\">10.5162/smsi2023/d7.4</a>.","short":"I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp. 253–524.","bibtex":"@inproceedings{Gräßler_Hieb_2023, title={Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}, DOI={<a href=\"https://doi.org/10.5162/smsi2023/d7.4\">10.5162/smsi2023/d7.4</a>}, booktitle={Lectures}, publisher={AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}, author={Gräßler, Iris and Hieb, Michael}, year={2023}, pages={253–524} }"},"_id":"52816","department":[{"_id":"152"}],"user_id":"5905","keyword":["synthetic training data","machine vision quality gates","deep learning","automated inspection and quality control","production control"],"language":[{"iso":"eng"}],"publication":"Lectures","type":"conference","abstract":[{"lang":"eng","text":"Manufacturing companies face the challenge of reaching required quality standards. Using\r\noptical sensors and deep learning might help. However, training deep learning algorithms\r\nrequire large amounts of visual training data. Using domain randomization to generate synthetic\r\nimage data can alleviate this bottleneck. This paper presents the application of synthetic\r\nimage training data for optical quality inspections using visual sensor technology. The results\r\nshow synthetically generated training data are appropriate for visual quality inspections."}],"status":"public"},{"department":[{"_id":"277"}],"user_id":"72850","_id":"20212","file_date_updated":"2020-10-27T13:31:01Z","article_number":"113432","type":"journal_article","status":"public","volume":140,"author":[{"last_name":"Prester","full_name":"Prester, Julian","first_name":"Julian"},{"full_name":"Wagner, Gerit","last_name":"Wagner","first_name":"Gerit"},{"first_name":"Guido","last_name":"Schryen","full_name":"Schryen, Guido","id":"72850"},{"first_name":"Nik Rushdi","last_name":"Hassan","full_name":"Hassan, Nik Rushdi"}],"date_updated":"2022-06-10T06:55:32Z","oa":"1","has_accepted_license":"1","intvolume":"       140","citation":{"apa":"Prester, J., Wagner, G., Schryen, G., &#38; Hassan, N. R. (2021). Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach. <i>Decision Support Systems</i>, <i>140</i>(January), Article 113432.","bibtex":"@article{Prester_Wagner_Schryen_Hassan_2021, title={Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach}, volume={140}, number={January113432}, journal={Decision Support Systems}, author={Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi}, year={2021} }","mla":"Prester, Julian, et al. “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.” <i>Decision Support Systems</i>, vol. 140, no. January, 113432, 2021.","short":"J. Prester, G. Wagner, G. Schryen, N.R. Hassan, Decision Support Systems 140 (2021).","ama":"Prester J, Wagner G, Schryen G, Hassan NR. Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach. <i>Decision Support Systems</i>. 2021;140(January).","ieee":"J. Prester, G. Wagner, G. Schryen, and N. R. Hassan, “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach,” <i>Decision Support Systems</i>, vol. 140, no. January, Art. no. 113432, 2021.","chicago":"Prester, Julian, Gerit Wagner, Guido Schryen, and Nik Rushdi Hassan. “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.” <i>Decision Support Systems</i> 140, no. January (2021)."},"language":[{"iso":"eng"}],"keyword":["Ideational impact","citation classification","academic recommender systems","natural language processing","deep learning","cumulative tradition"],"ddc":["000"],"publication":"Decision Support Systems","file":[{"file_size":440903,"file_name":"DECSUP-D-20-00312 - PREPUBLICATION.pdf","access_level":"open_access","file_id":"20213","date_updated":"2020-10-27T13:31:01Z","creator":"hsiemes","date_created":"2020-10-27T13:31:01Z","relation":"main_file","content_type":"application/pdf"}],"abstract":[{"text":"Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research has only recently developed automated citation classification techniques to distinguish between different types of citations and generally does not emphasize the conceptual content of the citations and its ideational impact. To address this problem, we develop Deep Content-enriched Ideational Impact Classification (Deep-CENIC) as the first automated approach for ideational impact classification to support researchers' literature search practices. We evaluate Deep-CENIC on 1,256 papers citing 24 information systems review articles from the IT business value domain. We show that Deep-CENIC significantly outperforms state-of-the-art benchmark models. We contribute to information systems research by operationalizing the concept of ideational impact, designing a recommender system for academic papers based on deep learning techniques, and empirically exploring the ideational impact of the IT business value domain.\r\n","lang":"eng"}],"date_created":"2020-10-27T13:28:21Z","title":"Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach","issue":"January","year":"2021"},{"_id":"24547","user_id":"60721","department":[{"_id":"196"},{"_id":"172"}],"keyword":["expected possession value","handball","tracking data","time series classification","deep learning"],"language":[{"iso":"eng"}],"type":"conference","publication":"8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)","abstract":[{"text":"Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time.","lang":"eng"}],"status":"public","date_updated":"2023-02-28T08:58:24Z","author":[{"first_name":"Oliver","id":"72849","full_name":"Müller, Oliver","last_name":"Müller"},{"first_name":"Matthew","last_name":"Caron","id":"60721","full_name":"Caron, Matthew"},{"first_name":"Michael","full_name":"Döring, Michael","last_name":"Döring"},{"first_name":"Tim","last_name":"Heuwinkel","full_name":"Heuwinkel, Tim"},{"orcid":"0000-0003-2683-5826","last_name":"Baumeister","id":"46","full_name":"Baumeister, Jochen","first_name":"Jochen"}],"date_created":"2021-09-16T08:33:04Z","title":"PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data","main_file_link":[{"url":"https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf"}],"conference":{"name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021)","start_date":"2021-09-13","end_date":"2021-09-17","location":"Online"},"publication_status":"inpress","year":"2021","citation":{"apa":"Müller, O., Caron, M., Döring, M., Heuwinkel, T., &#38; Baumeister, J. (n.d.). PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.","bibtex":"@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }","short":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.","mla":"Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>.","ieee":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data,” presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.","chicago":"Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” In <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>, n.d.","ama":"Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. In: <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>."}},{"status":"public","type":"conference","file_date_updated":"2020-09-22T06:36:00Z","department":[{"_id":"75"}],"user_id":"35343","_id":"19609","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - Subproject C4","_id":"16"}],"citation":{"apa":"Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili, R., &#38; Hecker, A. (2020). Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE.","short":"S.B. Schneider, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, R. Khalili, A. Hecker, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.","bibtex":"@inproceedings{Schneider_Manzoor_Qarawlus_Schellenberg_Karl_Khalili_Hecker_2020, title={Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2020} }","mla":"Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” <i>IEEE International Conference on Network and Service Management (CNSM)</i>, IEEE, 2020.","chicago":"Schneider, Stefan Balthasar, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, Ramin Khalili, and Artur Hecker. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” In <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE, 2020.","ieee":"S. B. Schneider <i>et al.</i>, “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning,” in <i>IEEE International Conference on Network and Service Management (CNSM)</i>, 2020.","ama":"Schneider SB, Manzoor A, Qarawlus H, et al. Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In: <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE; 2020."},"has_accepted_license":"1","author":[{"orcid":"0000-0001-8210-4011","last_name":"Schneider","id":"35343","full_name":"Schneider, Stefan Balthasar","first_name":"Stefan Balthasar"},{"full_name":"Manzoor, Adnan","last_name":"Manzoor","first_name":"Adnan"},{"first_name":"Haydar","last_name":"Qarawlus","full_name":"Qarawlus, Haydar"},{"last_name":"Schellenberg","full_name":"Schellenberg, Rafael","first_name":"Rafael"},{"last_name":"Karl","full_name":"Karl, Holger","id":"126","first_name":"Holger"},{"full_name":"Khalili, Ramin","last_name":"Khalili","first_name":"Ramin"},{"full_name":"Hecker, Artur","last_name":"Hecker","first_name":"Artur"}],"date_updated":"2022-01-06T06:54:08Z","oa":"1","file":[{"date_updated":"2020-09-22T06:36:00Z","date_created":"2020-09-22T06:29:16Z","creator":"stschn","file_size":642999,"file_id":"19610","file_name":"ris_with_copyright.pdf","access_level":"open_access","content_type":"application/pdf","relation":"main_file"}],"abstract":[{"text":"Modern services comprise interconnected components,\r\ne.g., microservices in a service mesh, that can scale and\r\nrun on multiple nodes across the network on demand. To process\r\nincoming traffic, service components have to be instantiated and\r\ntraffic assigned to these instances, taking capacities and changing\r\ndemands into account. This challenge is usually solved with\r\ncustom approaches designed by experts. While this typically\r\nworks well for the considered scenario, the models often rely\r\non unrealistic assumptions or on knowledge that is not available\r\nin practice (e.g., a priori knowledge).\r\n\r\nWe propose a novel deep reinforcement learning approach that\r\nlearns how to best coordinate services and is geared towards\r\nrealistic assumptions. It interacts with the network and relies on\r\navailable, possibly delayed monitoring information. Rather than\r\ndefining a complex model or an algorithm how to achieve an\r\nobjective, our model-free approach adapts to various objectives\r\nand traffic patterns. An agent is trained offline without expert\r\nknowledge and then applied online with minimal overhead. Compared\r\nto a state-of-the-art heuristic, it significantly improves flow\r\nthroughput and overall network utility on real-world network\r\ntopologies and traffic traces. It also learns to optimize different\r\nobjectives, generalizes to scenarios with unseen, stochastic traffic\r\npatterns, and scales to large real-world networks.","lang":"eng"}],"publication":"IEEE International Conference on Network and Service Management (CNSM)","language":[{"iso":"eng"}],"keyword":["self-driving networks","self-learning","network coordination","service coordination","reinforcement learning","deep learning","nfv"],"ddc":["006"],"year":"2020","title":"Self-Driving Network and Service Coordination Using Deep Reinforcement Learning","date_created":"2020-09-22T06:28:22Z","publisher":"IEEE"},{"language":[{"iso":"eng"}],"ddc":["000"],"keyword":["Deep Learning","Natural Language Processing","Aspect-based Sentiment Analysis"],"publication":"Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)","file":[{"content_type":"application/pdf","success":1,"relation":"main_file","date_updated":"2020-09-18T09:27:00Z","date_created":"2020-09-18T09:27:00Z","creator":"jkers","file_size":421780,"access_level":"closed","file_id":"19576","file_name":"Kersting & Geierhos (2020), Kersting2020.pdf"}],"abstract":[{"text":"This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.","lang":"eng"}],"date_created":"2020-01-15T08:35:07Z","publisher":"SCITEPRESS","title":"Aspect Phrase Extraction in Sentiment Analysis with Deep Learning","year":"2020","user_id":"58701","department":[{"_id":"579"}],"project":[{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"1","name":"SFB 901"},{"_id":"9","name":"SFB 901 - Subproject B1"}],"_id":"15580","file_date_updated":"2020-09-18T09:27:00Z","type":"conference","status":"public","author":[{"first_name":"Joschka","last_name":"Kersting","id":"58701","full_name":"Kersting, Joschka"},{"orcid":"0000-0002-8180-5606","last_name":"Geierhos","id":"42496","full_name":"Geierhos, Michaela","first_name":"Michaela"}],"date_updated":"2022-01-06T06:52:29Z","conference":{"name":"International Conference on Agents and Artificial Intelligence (ICAART) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI)","location":"Valetta, Malta"},"has_accepted_license":"1","citation":{"apa":"Kersting, J., &#38; Geierhos, M. (2020). Aspect Phrase Extraction in Sentiment Analysis with Deep Learning. In <i>Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)</i> (pp. 391--400). Setúbal, Portugal: SCITEPRESS.","bibtex":"@inproceedings{Kersting_Geierhos_2020, place={Setúbal, Portugal}, title={Aspect Phrase Extraction in Sentiment Analysis with Deep Learning}, booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)}, publisher={SCITEPRESS}, author={Kersting, Joschka and Geierhos, Michaela}, year={2020}, pages={391--400} }","mla":"Kersting, Joschka, and Michaela Geierhos. “Aspect Phrase Extraction in Sentiment Analysis with Deep Learning.” <i>Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)</i>, SCITEPRESS, 2020, pp. 391--400.","short":"J. Kersting, M. Geierhos, in: Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020), SCITEPRESS, Setúbal, Portugal, 2020, pp. 391--400.","ama":"Kersting J, Geierhos M. Aspect Phrase Extraction in Sentiment Analysis with Deep Learning. In: <i>Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)</i>. Setúbal, Portugal: SCITEPRESS; 2020:391--400.","chicago":"Kersting, Joschka, and Michaela Geierhos. “Aspect Phrase Extraction in Sentiment Analysis with Deep Learning.” In <i>Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)</i>, 391--400. Setúbal, Portugal: SCITEPRESS, 2020.","ieee":"J. Kersting and M. Geierhos, “Aspect Phrase Extraction in Sentiment Analysis with Deep Learning,” in <i>Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)</i>, Valetta, Malta, 2020, pp. 391--400."},"page":"391--400","place":"Setúbal, Portugal"},{"status":"public","abstract":[{"text":"In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.","lang":"eng"}],"type":"conference","publication":"Parallel Problem Solving from {Nature} (PPSN XVI)","language":[{"iso":"eng"}],"extern":"1","keyword":["Automated algorithm selection","Deep learning","Feature-based approaches","Traveling Salesperson Problem"],"user_id":"102979","department":[{"_id":"819"}],"_id":"48897","citation":{"ama":"Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>. Springer-Verlag; 2020:48–64. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>","ieee":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 2020, pp. 48–64, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","chicago":"Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” In <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>.","bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin, Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>}, booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={48–64} }","mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 48–64, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 48–64.","apa":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>"},"page":"48–64","place":"Berlin, Heidelberg","year":"2020","publication_identifier":{"isbn":["978-3-030-58111-4"]},"doi":"10.1007/978-3-030-58112-1_4","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","date_created":"2023-11-14T15:59:00Z","author":[{"last_name":"Seiler","full_name":"Seiler, Moritz","first_name":"Moritz"},{"last_name":"Pohl","full_name":"Pohl, Janina","first_name":"Janina"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"publisher":"Springer-Verlag","date_updated":"2023-12-13T10:49:45Z"}]