Publikationen

@article{MaiNey2024a,
author = {Maier, Hannah Benedictine and Neyazi, Alexandra and Bundies, Gabriel L. and Meyer-Bockenkamp, Fiona and Bleich, Stefan and Pathak, Hansi and Ziert, Yvonne and Neuhaus, Barbara and M{ü}ller, Franz-Josef and Pollmann, Iris and Illig, Thomas and M{ü}cke, Stefanie and M{ü}ller, Meike and M{ö}ller, Brinja Kira and Oeltze-Jafra, Steffen and Kacprowski, Tim and Voges, Jan and M{ü}ntefering, Fabian and Scheiber, Josef and Reif, Andreas and Aichholzer, Mareike and Reif-Leonhard, Christine and Schmidt-Kassow, Maren and Hegerl, Ulrich and Reich, Hanna and Unterecker, Stefan and Weber, Heike and Deckert, J{ü}rgen and B{ö}ssel-Debbert, Nicole and Grabe, Hans J. and Lucht, Michael and Frieling, Helge},
journal = {Trials},
keywords = {BDNF},
month = {04},
number = 247,
title = {Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients—a randomized rater-blinded trial},
volume = 25,
year = 2024
}

@article{KluBra2024a,
author = {Kluger, Florian and Brachmann, Eric and Yang, Michael Ying and Rosenhahn, Bodo},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {fitting},
month = {03},
title = {Robust Shape Fitting for 3D Scene Abstraction},
year = 2024
}

@inproceedings{KluRos2024a,
author = {Kluger, Florian and Rosenhahn, Bodo},
booktitle = {AAAI},
keywords = {PARSAC},
month = {02},
title = {PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus},
year = 2024
}

@article{AdhVog2023a,
author = {Adhisantoso, Yeremia Gunawan and Voges, Jan},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {M62859},
month = {04},
title = {Cross-check of M62859 Results on Updated CE Results for Annotation Data Indexing Using B-Tree},
year = 2023
}

@article{Poker_2023,
author = {Poker, Yvonne and von Hardenberg, Sandra and Hofmann, Winfried and Tang, Ming and Baumann, Ulrich and Schwerk, Nicolaus and Wetzke, Martin and Lindenthal, Viola and Auber, Bernd and Schlegelberger, Brigitte and Ott, Hagen and von Bismarck, Philipp and Viemann, Dorothee and Dressler, Frank and Klemann, Christian and Bergmann, Anke Katharina},
journal = {Frontiers in Genetics},
keywords = {l3s},
month = {01},
publisher = {Frontiers Media {SA}},
title = {Systematic genetic analysis of pediatric patients with autoinflammatory diseases},
volume = 14,
year = 2023
}

@inproceedings{nandy2023textbfemphclmsm,
author = {Nandy, Abhilash and Kapadnis, Manav Nitin and Goyal, Pawan and Ganguly, Niloy},
booktitle = {The 2023 Conference on Empirical Methods in Natural Language Processing},
keywords = {pretraining},
title = {CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text},
year = 2023
}

@inproceedings{LeeWan2023,
author = {Lee, Chang-Shing and Wang, Mei-Hui and Chen, Chih-Yu and Yang, Fu-Jie and Dockhorn, Alexander},
booktitle = {2023 IEEE Congress on Evolutionary Computation},
keywords = {Student},
note = {(to be published)},
pages = {1-8},
title = {Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience},
year = 2023
}

@article{AwiDoc2023,
author = {Awiszus, Maren and Dockhorn, Alexander and Hoover, Amy K. and Liapis, Antonios and Lucas, Simon M. and Eladhari, Mirjam Palosaari and Schrum, Jacob and Volz, Vanessa},
journal = {Human-Game AI Interaction (Dagstuhl Seminar 22251)},
keywords = {Language},
month = {01},
number = 6,
pages = {34-37},
title = {Language Models for Procedural Content Generation},
volume = 12,
year = 2023
}

@article{DocEbe2023a,
author = {Dockhorn, Alexander and Eberhardinger, Manuel and Loiacono, Daniele and Liebana, Diego Perez and Veltkamp, Remco},
journal = {Human-Game AI Interaction (Dagstuhl Seminar 22251)},
keywords = {Pokegen},
month = {01},
number = 6,
pages = {39-42},
title = {Pokegen},
volume = 12,
year = 2023
}

@inproceedings{KaiRei2023a,
author = {Kaiser, Timo and Reinders, Christoph and Rosenhahn, Bodo},
booktitle = {Computer Vision and Pattern Recognition Workshops (CVPRW)},
keywords = {Segmention},
month = {06},
title = {Compensation Learning in Semantic Segmentation},
year = 2023
}

@inproceedings{noauthororeditor,
abstract = {Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.},
author = {Roy, Soumyadeep and Wallat, Jonas and Sundaram, Sowmya S and Nejdl, Wolfgang and Ganguly, Niloy},
keywords = {l3s},
pages = {2002-2009},
series = {Frontiers in Artificial Intelligence and Applications},
title = {GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning},
volume = 372,
year = 2023
}

@article{BenEim2023,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and D{ö}hler, Sebastian and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {Transactions on Machine Learning Research},
keywords = {reinforcement},
month = {06},
title = {Contextualize Me - The Case for Context in Reinforcement Learning},
year = 2023
}

@misc{https://doi.org/10.48550/arxiv.2206.05447,
author = {Moosbauer, Julia and Casalicchio, Giuseppe and Lindauer, Marius and Bischl, Bernd},
keywords = {leibnizailab},
publisher = {arXiv},
title = {Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution},
year = 2022
}

@article{RN11,
author = {Mast, M. and Marschollek, M. and Jack, T. and Wulff, A. and Elise Study, Group},
journal = {Stud Health Technol Inform},
keywords = {l3s},
pages = {228-231},
title = {Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data},
type = {Journal Article},
volume = 289,
year = 2022
}

@article{https://doi.org/10.48550/arxiv.2202.04500,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {Arxiv Preprint},
keywords = {leibnizailab},
publisher = {arXiv},
title = {Contextualize Me -- The Case for Context in Reinforcement Learning},
year = 2022
}

@inproceedings{ReiSch2022,
author = {Reinders, Christoph and Schubert, Frederik and Rosenhahn, Bodo},
booktitle = {Arxiv Preprint},
keywords = {Feature},
month = {03},
title = {ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing},
year = 2022
}

@article{GriKuh2022,
author = {Grimm, Eckhard and Kuhnke, Felix and Gajdt, Anna and Ostermann, J{ö}rn and Knoche, Moritz},
journal = {Horticulturae},
keywords = {Flesh},
month = {01},
number = 2,
title = {Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis},
volume = 8,
year = 2022
}

@inproceedings{EggMue2021,
author = {Eggensperger, Katharina and M{ü}ller, Philipp and Mallik, Neeratyoy and Feurer, Matthias and Sass, René and Klein, Aaron and Awad, Noor and Lindauer, Marius and Hutter, Frank},
booktitle = {Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)},
keywords = {HPOBench},
month = 12,
title = {HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO},
year = 2021
}

@inproceedings{Zhao_2021,
abstract = {To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.},
author = {Zhao, Bo and van der Aa, Han and Nguyen, Thanh Tam and Nguyen, Quoc Viet Hung and Weidlich, Matthias},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
keywords = {l3s},
month = {06},
publisher = {{ACM}},
title = {{EIRES}: Efficient Integration of Remote Data in Event Stream Processing},
year = 2021
}

@article{holzapfel2021association,
abstract = {Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs.},
author = {Holzapfel, C and Sag, S and Graf-Schindler, J and Fischer, M and Drabsch, T and Illig, T and Grallert, H and Stecher, L and Strack, C and Caterson, ID and Jebb, SA and Hauner, H and Baessler, A},
journal = {Nutrients},
keywords = {l3s},
number = 3,
publisher = {MDPI},
title = {Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis},
type = {Publication},
volume = 13,
year = 2021
}

@conference{wu2021hashingaccelerated,
abstract = {Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.},
author = {Wu, Wei and Li, Bin and Luo, Chuan and Nejdl, Wolfgang},
keywords = {l3s},
note = {cite arxiv:2105.14280},
title = {Hashing-Accelerated Graph Neural Networks for Link Prediction},
year = 2021
}

@proceedings{J,
abstract = {The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT–WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets.},
author = {Kaushal, Ayush and Saha, Avirup and Ganguly, Niloy},
keywords = {leibnizailab},
month = {06},
pages = {3879-3889},
title = {tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets},
year = 2021
}

@inproceedings{PesOst2021a,
author = {Pestel-Schiller, Ulrike and Ostermann, J{ö}rn},
booktitle = {13th European Conference on Synthetic Aperture Radar},
keywords = {Compressed},
month = {03},
title = {Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability},
year = 2021
}

@inproceedings{BenEim2021a,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
booktitle = {NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning},
keywords = {Reinforcement},
month = 12,
title = {CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning},
year = 2021
}

@inproceedings{9647067,
author = {Ilyas, Zaid and Sharif, Naeha and Schousboe, John T. and Lewis, Joshua R. and Suter, David and Gilani, Syed Zulqarnain},
booktitle = {2021 Digital Image Computing: Techniques and Applications (DICTA)},
keywords = {leibnizailab},
pages = {01-07},
title = {GuideNet: Learning Inter- Vertebral Guides in DXA Lateral Spine Images},
year = 2021
}

@inproceedings{PesHu2021a,
author = {Pestel-Schiller, Ulrike and Hu, Kai and Gritzner, Daniel and Ostermann, J{ö}rn},
booktitle = {11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Paper 15},
keywords = {Relevant},
month = {03},
title = {Determination of Relevant Hyperspectral Bands Using a Spectrally Constrained CNN},
year = 2021
}

@article{KnuKlu2021,
author = {Knura, Martin and Kluger, Florian and Zahtila, Moris and Schiewe, Jochen and Rosenhahn, Bodo and Burghardt, Dirk},
journal = {ISPRS International Journal of Geo-Information},
keywords = {Bicycle},
month = 10,
title = {Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden},
year = 2021
}

@inproceedings{LinEgg2021,
author = {Lindauer, Marius and Eggensperger, Katharina and Feurer, Matthias and Biedenkapp, André and Deng, Difan and Benjamins, Carolin and Sass, René and Hutter, Frank},
booktitle = {ArXiv: 2109.09831},
keywords = {SMAC3},
title = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
year = 2021
}

@inproceedings{HorKai2021,
author = {Hornakova*, Andrea and Kaiser*, Timo and Rolinek, Michal and Rosenhahn, Bodo and Swoboda, Paul and Henschel, Roberto and equal contribution), (*},
booktitle = {International Conference on Computer Vision (ICCV)},
keywords = {Scalable},
month = 10,
title = {Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths},
year = 2021
}

@inproceedings{BenMeu2021,
author = {Benjak, Martin and Meuel, Holger and Laude, Thorsten and Ostermann, J{ö}rn},
booktitle = {2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2021)},
keywords = {for},
month = {04},
note = {accepted for publication},
title = {Enhanced Machine Learning-based Inter Coding for VVC},
year = 2021
}

@article{SchAwi2021,
author = {Schubert, Frederik and Awiszus, Maren and Rosenhahn, Bodo},
journal = {IEEE Transactions on Games},
keywords = {TOAD-GAN},
month = {03},
note = {10 pages, 14 figures.},
title = {TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games},
year = 2021
}

@article{HutFuk2021,
author = {Hutter, Frank and Fuks, Lior and Lindauer, Marius and Awad, Noor},
keywords = {strategy},
title = {Method, device and computer program for producing a strategy for a robot},
year = 2021
}

@inproceedings{BMFcvpr2021,
author = {Tennakoon, Ruwan and Suter, David and Zhang, Erchuan and Chin, Tat-Jun and Bab-Hadiashar, Alireza},
booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
keywords = {leibnizailab},
note = {Oral Presentation (17\% of accepted papers, roughly 3\% of sumitted papers)},
pages = {2865-2874},
title = {Consensus Maximisation Using Influences of Monotone Boolean Functions},
year = 2021
}

@inproceedings{KluAck2021a,
author = {Kluger, Florian and Ackermann, Hanno and Brachmann, Eric and Yang, Michael Ying and Rosenhahn, Bodo},
booktitle = {CVPR},
keywords = {Cuboids},
month = {06},
title = {Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images},
year = 2021
}

@inproceedings{RumEhl2021,
author = {Rumberg, Lars and Ehlert, Hanna and L{ü}dtke, Ulrike and Ostermann, J{ö}rn},
booktitle = {Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association},
keywords = {Recognition},
month = {08},
title = {Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning},
year = 2021
}

@inproceedings{HacKru2021a,
author = {Hachmann, Hendrik and Kr{ü}ger, Benjamin and Rosenhahn, Bodo and Nogueira, Waldo},
booktitle = {International Symposium on Biomedical Imaging, ISBI},
keywords = {Computed},
month = {04},
title = {Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation},
year = 2021
}

@inproceedings{EimBie2021b,
author = {Eimer, Theresa and Biedenkapp, André and Reimer, Maximilian and Adriaensen, Steven and Hutter, Frank and Lindauer, Marius},
booktitle = {Proceedings of the international joint conference on artificial intelligence (IJCAI)},
keywords = {leibnizailab},
month = {08},
note = {To appear.},
title = {DACBench: A Benchmark Library for Dynamic Algorithm Configuration},
year = 2021
}

@inproceedings{GueHau2021a,
author = {Guerrero-Viu, Julia and Hauns, Sven and Izquierdo, Sergio and Miotto, Guilherme and Schrodi, Simon and Biedenkapp, Andre and Elsken, Thomas and Deng, Difan and Lindauer, Marius and Hutter, Frank},
booktitle = {Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21},
keywords = {Optimization},
month = {07},
title = {Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization},
year = 2021
}

@inproceedings{SchEim2021b,
author = {Schubert, Frederik and Eimer, Theresa and Rosenhahn, Bodo and Lindauer, Marius},
booktitle = {Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML)},
keywords = {Reinforcement},
month = {07},
title = {Towards Automatic Risk Adaption in Distributional Reinforcement Learning},
year = 2021
}

@inproceedings{EimBie2021a,
author = {Eimer, Theresa and Biedenkapp, Andre and Hutter, Frank and Lindauer, Marius},
booktitle = {Proceedings of the international conference on machine learning (ICML)},
keywords = {Reinforcement},
month = {07},
note = {To appear},
title = {Self-Paced Context Evaluation for Contextual Reinforcement Learning},
year = 2021
}

@article{NarHen2021,
author = {Narisetti, Narendra and Henke, Michael and Seiler, Christiane and Junker, Astrid and Ostermann, J{ö}rn and Altmann, Thomas and Gladilin, Evgeny},
journal = {Scientific Reports},
keywords = {analysis},
month = {08},
title = {Fully-automated root image analysis (faRIA)},
volume = 11,
year = 2021
}

@inproceedings{HuIos2020,
author = {Hu, Tongxin and Iosifidis, Vasileios and Wentong, Liao and Hang, Zhang and Yang, Michael Ying and Ntoutsi, Eirini and Rosenhahn, Bodo},
booktitle = {23rd International Conference on Discovery Science},
keywords = {for},
month = 10,
note = {Code available: https://github.com/wtliao/FairNN},
title = {FairNN - Conjoint Learning of Fair Representations for Fair Decisions},
year = 2020
}

@article{OstHin2020a,
author = {Ostermann, J{ö}rn and Hinrichs, Reemt},
journal = {Unimagazin},
keywords = {und},
month = {06},
number = 1,
title = {Links und rechts verbinden},
year = 2020
}

@inproceedings{SamOst2020,
author = {Samayoa, Yasser and Ostermann, J{ö}rn},
booktitle = {IEEE Latin-American Conference on Communications (LATINCOM 2020)},
keywords = {System},
month = 11,
pages = 5,
title = {Parameter Selection for a Video Communication System based on HEVC and Channel Coding},
year = 2020
}

@inproceedings{AwiSch2020,
author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
booktitle = {AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award},
keywords = {TOAD-GAN},
month = 10,
note = {7 pages, 7 figures.},
title = {TOAD-GAN: Coherent Style Level Generation from a Single Example},
year = 2020
}

@incollection{Hu_2020,
author = {Hu, Tongxin and Iosifidis, Vasileios and Liao, Wentong and Zhang, Hang and Yang, Michael Ying and Ntoutsi, Eirini and Rosenhahn, Bodo},
booktitle = {Discovery Science},
keywords = {l3s},
pages = {581-595},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {FairNN - Conjoint Learning of Fair Representations for Fair Decisions.},
type = {Publication},
volume = 12323,
year = 2020
}

@inproceedings{JueHin2020,
author = {J{ü}rgens, Henrik and Hinrichs, Reemt and Ostermann, J{ö}rn},
booktitle = {Proceedings of the DAFx2020 (Vol I)},
keywords = {Settings},
title = {Recognizing Guitar Effects and Their Parameter Settings},
year = 2020
}

@article{GraAck2020,
author = {Gra{{\"s}}hof, Stella and Ackermann, Hanno and Brandt, Sami and Ostermann, J{ö}rn},
journal = {Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
keywords = {Faces},
month = {04},
note = {early access},
title = {Multilinear Modelling of Faces and Expressions},
year = 2020
}

@inproceedings{chauDICTA2020,
author = {Minh, Chau Nguyen Duc and Gilani, Syed Zulqarnain and Islam, Syed and Suter, David},
booktitle = {DICTA2020},
keywords = {leibnizailab},
title = {Learning Affordance Segmentation: An Investigative Study},
year = 2020
}

@article{TanPsych2019,
author = {Tan, Diana and Maybery, Murray and Gilani, Syed Zulqarnain and Alvares, Gail and Mian, Ajmal and Suter, David and Whitehouse, Andrew},
journal = {Translational Psychiatry},
keywords = {leibnizailab},
number = 1,
title = {A broad autism phenotype expressed in facial morphology},
volume = 10,
year = 2020
}